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J. COMBIN. MATH. COMBIN. COMPUT. 127a (2025) 2179--2197

Journal of Combinatorial Mathematics and
Combinatorial Computing
www.combinatorialpress.com/jcmcc

Knowledge graph construction for intelligent
education and its application in training

physical

Hong Li1,✉
1

School of Physical Education, Guizhou University of Engineering Science, Bijie, Guizhou, 551700, China

ABSTRACT

Knowledge mapping, as an emerging knowledge management tool, provides a new perspective of
knowledge learning for physical education teaching. In this study, knowledge mapping is introduced into
physical education teaching, and a comprehensive physical education knowledge map is constructed by
integrating the teaching resources and contents of physical education teaching and utilizing related
techniques such as knowledge extraction and knowledge fusion. The method of fusion of sports
knowledge graph is also proposed, including three parts: graph approximation, similarity calculation, and
subgraph fusion. Finally, the constructed knowledge graph is practically applied, and a recommendation
model based on sports knowledge graph and neural network is constructed to realize the sports teaching
application of intelligent educational knowledge graph. The entity recognition module optimized the
recognition accuracy rate on the objective existence entities of sports by 1.45%, and the relationship
extraction module outperformed AGGCN in all three indicators. The training method of this paper is
better than the MICT sports training method in improving students' cardiorespiratory capacity and
flexibility quality. The improvement of students' 800m running performance under this paper's training
program is 0.13min more than that of MICT.It is proved that the sports course recommendation model
based on knowledge graph and neural network provides a reference for the management and application
of knowledge data in physical education, with a view to promoting the progress in the field of intelligent
education.
Keywords: Physical education knowledge graph, Physical education, Recommender system, Knowledge
fusion, Knowledge extraction

1. Introduction
Intelligent sports refers to the general term for the advanced technologies, methods and means, and related
hardware and software equipment, rules and regulations that are adopted to realize the intelligent,
modernized, and efficient development of sports by relying on various types of hardware equipment, and
by means of informatization, networking, and digitization, and by applying technologies such as the
✉Corresponding author.
E-mail address: lhongjq@163.com (H. Li).
Received 01 September 2024; Revised 08 November 2024; Accepted 31 December 2024; Published Online 15 April 2025.
DOI: 10.61091/jcmcc127a-126
© 2025 The Author(s). Published by Combinatorial Press. This is an open access article under the CC BY license
(https://creativecommons.org/licenses/by/4.0/).

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Internet, the Internet of Things, big data, and cloud computing [1-4]. As early as in the early 1990s, along
with the widespread use of computers, intelligent technology began to be gradually applied and popularized
in the field of sports. The earliest technologies such as electric timing and laser ranging, which appeared
in international competitive sports venues, opened the precedent of intelligentization in the sports field [58]. In the past 30 years, China's sports field and intelligent technology synchronous development, digital,
networked intelligent state-of-the-art technology, has been widely used in all areas of the sports industry,
to promote the revolutionary and breakthrough development of sports [9-11]. The birth of intelligent
sports has brought revolutionary development for Chinese sports and injected powerful impetus for the
rapid development of sports and sports industry. Knowledge graph is a key part of intelligent sports
education [12-14].
Knowledge graph is a structured knowledge representation method, which abstracts and summarizes
various concepts, entities and relationships in reality, and represents them through nodes and edges,
presenting a graphical knowledge network and enabling semantic level understanding and reasoning [1518]. Among them, nodes represent entities or concepts, and edges represent the connections and
relationships between them. Through the unified modeling and maintenance of the graph, efficient
organization and sharing of knowledge can be achieved [19-21]. Knowledge graph can model and
represent various knowledge in a unified way, realize the integration and sharing of cross-domain
knowledge in a structured way, and provide learners with richer and more comprehensive learning
resources [22-24].
The construction process of sports knowledge graph includes four processes: construction of sports
knowledge ontology, knowledge extraction, knowledge fusion and knowledge graph visualization. In this
paper, BERT-BiLSTM-CRF and TextCNN relational extraction model fusing BERT model and attention
mechanism are proposed to accomplish two parts of knowledge extraction, namely, named entity
recognition and entity relational extraction, respectively. Then the framework structure of sports
knowledge fusion is proposed, including three parts: data preprocessing, similarity calculation and
knowledge fusion. Data preprocessing is the approximation of knowledge triples. In addition, the similarity
algorithm of graphs is utilized to compute the knowledge and get more effective knowledge subgraphs.
Finally, the knowledge fusion result is obtained by using the double neighbor matrix. The data related to
the sports knowledge graph is stored and visualized using the D3 library. Based on this, a sports training
program recommendation model fusing knowledge graph and neural network is proposed to accomplish
the task of intelligent sports teaching.

2. Sports knowledge mapping
2.1.

Theories related to knowledge graph construction

Knowledge graph [25] is a structured knowledge base that is presented in the form of a graph that
expresses semantic relationships between entities through a network graph composed of nodes and edges.
In this network, each node represents an independent entity, and the edges connecting the nodes reveal
diverse relationships or attributes between entities. This representation not only clearly depicts the direct
connections between entities, but also further refines the characteristics and states of entities through the
properties of edges.
The process of knowledge graph construction includes four processes: ontology design, data set
construction, knowledge extraction and knowledge graph visualization, which are divided into two
categories: schema layer and data layer. The schema layer, also known as the ontology layer, is equivalent
to the “blueprint” of the knowledge graph, which specifies the specific existence categories of entities and
the types of relationships between entities. The design of schema layer needs to take into account the future
scalability and flexibility, which is crucial for the organization and scalability of the whole knowledge graph.
In the construction process of the knowledge graph, the schema layer and the data layer jointly determine
the structure and content, the schema layer provides structured guidance for the data layer, while the data

KNOWLEDGE GRAPH CONSTRUCTION FOR INTELLIGENT PHYSICAL

2181

layer fills in the structure of the schema layer, and the two complement each other to form a complete
knowledge graph.
The role of ontology in knowledge mapping is to facilitate the sharing and reuse of knowledge between
systems for effective information exchange. In constructing knowledge graphs, ontologies provide a
framework for defining terms and concepts and their interrelationships, and this framework not only
elaborates entities and their attributes, but also includes a variety of relationships between
entities.Ontologies are widely used in the fields of information retrieval, data integration, intelligent Q&A,
recommender systems, and semantic web. In information retrieval, ontology can improve the accuracy and
relevance of search engines; in data integration, ontology can integrate data from different sources; in
intelligent Q&A, ontology can help the system to understand the user's query and provide accurate answers;
in recommender systems, ontology can provide personalized recommendations based on the user's interests
and behaviors; and in the construction of semantic networks, ontology is the construction of the knowledge
network's In the construction of semantic network, ontology is the foundation for constructing knowledge
network. In short, ontology is the cornerstone of knowledge graph, which provides the structure for the
organization of knowledge, as well as the basis for the reasoning and application of knowledge. With the
development of artificial intelligence and big data technology, the role of ontology in knowledge graph will
become more important, and it will provide powerful support for us to understand the complex world and
build intelligent systems.
Knowledge extraction [26] aims to mine valuable knowledge from diverse data sources to enhance data
availability and reusability, and to provide a basis for knowledge reasoning and application. The input data
for knowledge extraction can be structured, unstructured, or semi-structured. Knowledge extraction
includes tasks such as named entity recognition, relationship extraction, and event extraction. Named
entity recognition refers to identifying entities with specific types and attributes from data. Relationship
extraction refers to identifying semantic relationships between entities from the data. Event extraction
refers to identifying events with specific types and participants from the data. These tasks are
interdependent and interact with each other, and together they form a complete knowledge extraction
process. Knowledge extraction is not only a technical activity of data processing, but it also involves a
number of fields such as natural language processing, machine learning, and artificial intelligence. With
the continuous progress of technology, knowledge extraction will become more accurate and efficient,
which will greatly improve the efficiency and quality of information retrieval and help users find the
knowledge they really need in the huge ocean of information.
Knowledge graph visualization is a technology that converts complex data structures in a knowledge
graph into intuitive graphics through a graphical interface. These graphs usually include nodes (entities)
and edges (relationships between entities), and different types of nodes and edges can be represented by
different colors, sizes and shapes. Knowledge graph visualization not only helps users to quickly
understand the complex relationships between entities, but also enhances knowledge dissemination and
application by making it easier to explore different data connections and patterns through an interactive
interface.
Data visualization makes complex information more intuitive and understandable, while graph databases
provide powerful data management and query capabilities. As a back-end storage, graph databases can
efficiently manage and process large amounts of knowledge graph data. The combination of knowledge
graph visualization and graph database not only provides an effective solution for the management, query
and display of knowledge graph data, but also enhances the user's interactive experience and promotes indepth understanding and application of knowledge.
2.2.

Sport ontology construction methods

An ontology is a rule established for modeling concepts, an abstract description of the objective world,
which gives a clear definition of concepts and their connections through formalization.
1) Determine the goal of construction. The goal of constructing a basic education sports knowledge map

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is to reconstruct the connections between knowledge points from a large amount of unorganized
educational information resources, and then assist students to quickly sound their sports knowledge system.
2) Define the domain and scope of the ontology. To ensure that the constructed ontology contains
comprehensive and accurate content and can be integrated with teaching practice, a formal representation
of sports knowledge points and knowledge point relationships is realized.
3) Determine the set of core knowledge concepts. The core knowledge concepts should have in-depth
intrinsic connection and logical structure, and be able to connect other knowledge points and concepts in
sports.
4) Establish the class hierarchy. In this paper, multiple instances with the same characteristics are
abstracted into one concept, which corresponds to a class in the ontology model. Usually, the higher the
level of the class, the more abstract the concept it represents, and the lower the level the more specific the
knowledge it represents.
5) Define attributes and constraints. Establish the semantic relationship between knowledge points and
improve the definition of data attributes and relationship attributes of knowledge points.
6) Create instances. After determining the class hierarchy as well as attributes in the ontology, this paper
will select the ontology building tool to add class instances, attributes, etc. Use the OWL ontology
description language to create the ontology class hierarchy, and add the data properties of the class by
using the Data properties panel under the Entities tab, and finish setting the type and definition domain of
each property.
2.3.

Sports knowledge extraction

Next, the knowledge extraction method will be designed based on the deep learning model, aiming to
improve the efficiency and accuracy of knowledge graph construction by efficiently completing the
construction of the data layer in an automated manner. Among them, knowledge extraction mainly
includes named entity recognition and entity relationship extraction.
2.3.1. Named Entity Recognition Based on BERT-BiLSTM-CRF. Named entity recognition is an
important part of the knowledge extraction task, which mainly relies on sequence annotation methods. In
this paper, based on the BiLSTM-CRF [27] model, BERT [28] is introduced to pre-train the language
model in order to reduce the cost of manual annotation and at the same time improve the accuracy of model
training.
The model consists of four modules, which are input layer, BERT layer, BiLSTM layer and CRF layer.
1) BERT layer. As the BERT model can effectively capture the rich semantic information between words
and the contextual features of sentence context through pre-training on large-scale text corpus, the main
task of the BERT model is to transform the input specific text sequences into vectors that can be recognized
and operated by the computer, which can be used as inputs for the model in the next layer.
2) BiLSTM layer. BiLSTM is a model based on recurrent neural network, which is a combination of
forward LSTM and backward LSTM, and can effectively capture the contextual information of each
position in the sequence by performing forward and backward processing simultaneously.
3) CRF layer. BiLSTM model can effectively process long distance text information, modeling and learning
the information of the context in the input sequence, and extracting the overall information characteristics
of the sentence. However, the BiLSTM model does not directly consider the dependencies between the
labels, resulting in the output labels do not contain the labeling features of the words that have been output
in the context, and can not be composed into a complete entity, so a CRF layer is needed to carry out the
actual labeling process.
If the input sequence is X   x1 , x2 , xn  , given the tagged sequence Y   y1 , y2 , yn  , the model
computes the score for the tagged sequence as shown in equation (1):

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KNOWLEDGE GRAPH CONSTRUCTION FOR INTELLIGENT PHYSICAL
n

n

i 1

i 0

Score  X , Y    Pi , yi   Ayi1 , yi

(1)

The CRF model is trained using a maximized log-likelihood function to compute the conditional
probability of a tag sequence Y given sentence X , where YX is the given sentence X all possible tag
sequences, and log P Y | X  is the loss function.

P Y | X  

exp  Score( X , Y ) 

 exp  Score( X , Y ) 

(2)

YˆY X



log P(Y | X )  score( X , Y )  log   exp Score( X , Y ) 
 YˆY X






(3)

In model training, the Viterbi algorithm is used to solve for the globally optimal sequence, where Y *
is the sequence in the set that maximizes the partition function.



Y *  arg max score
X ,Y
X
YˆY



(4)

2.3.2. Entity Relationship Extraction Based on BERT-TextCNN-Attention. In this paper, we propose a
TextCNN Chinese relationship extraction model that integrates the BERT pre-training model and the
attention mechanism to accomplish the task of extracting text entity relationships.
Firstly, the organized Chinese text data are converted into input vectors and input into the BERT pretraining model. Then the feature vectors at different levels are extracted. Subsequently, the feature vectors
after weight redistribution are obtained by calculating the attention probability distribution. Finally, the
feature vectors output from the attention layer are averagely pooled through the pooling layer, and the
obtained dimensionality reduction vectors are passed through the fully connected layer and softmax
classifier to generate the final relationship extraction results.
1) Input layer. BERT is a powerful pre-trained semantic representation model constructed based on a
multilayer bidirectional Transformer encoder, which can simultaneously extract the bidirectional semantic
information of the context, has a strong semantic representation of word vectors, and is widely used in
various natural language processing tasks.
2) TextCNN Convolutional Layer. The last layer of the BERT model outputs a feature vector matrix H
of dimension n 768 as the input of the TextCNN layer, and designs a filter window W of dimension
h  k , where k is the dimension of the word vector, i.e., here k  768 and h represent the number of
words contained in the window. By continuously sliding the window, each time when sliding a position,
complete the calculation as in equation (5) can get the new local features of the text.
ci  f Wm  H ij  h 1  b 

(5)

ci is the feature vector obtained after the convolution operation, f is the nonlinear activation function,
b is the bias term, Ww is the convolution kernel of many different sizes, and Hi:i h1 denotes the word
vector matrix H consisting of submatrices from rows i to i  h 1. Thus for a sentence of length n , a

filter window of dimension h  k will be able to generate n  h  1 new features to form the
corresponding set of feature mappings Cn :
Cm  c1 , c2 ,

, cnk 1 

(6)

3) Self-attention layer. Self-attention mechanism is introduced in TextCNN to further improve the weight
of more important features in the relationship extraction task and realize the enhancement of the
association information between entities in the text. For each kind of convolutional kernel processing to
obtain the feature matrix C for self-attention calculation, the specific calculation method is as follows.

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Firstly, the self-attention mechanism is used for encoding, and the attention distribution of the input
matrix can be obtained as follows:

Q  WQ C

(7)

K  WK C

(8)

V  WV C

(9)

Where, WQ , WK , WV are the weight matrices of the learnable parameters. Then, the attention weights
are normalized to the [0,1] interval by softmax function, and the calculation formula is shown in (10):

 QK T 
Self  Attention  Q, K ,V   sof max 
V
 d 
k 


(10)

4) Pooling Layer and Output Layer. In this paper, maximum pooling operation is adopted. The maximum
value in the feature matrix is extracted so as to replace the whole feature matrix and capture the most
important features for each feature matrix, this pooling method can efficiently deal with the text length
with variations.
P  max  M 

(11)

The results of the pooling operation are input into the fully connected layer, and the softmax function is
used to calculate the predicted probability, obtaining the category with the highest score as the result of
the relationship extraction:



lable  agr max soft max W f P  b f 



(12)

where W f denotes the weight of the fully connected layer, b f is the bias term, and softmax is the
activation function.
2.4.

Integration of sports knowledge

In order to construct a more accurate sports knowledge graph, this chapter firstly proposes the general
framework of sports knowledge graph, and the detailed design and realization of each segment in the
framework.
2.4.1. Sports Knowledge Integration Architecture. The construction of sports knowledge graph relies
on the fusion of knowledge. The knowledge fusion can integrate the scattered and redundant knowledge
through knowledge fusion, which is one of the key processes in the construction of sports knowledge graph.
Therefore, this paper proposes the framework structure of sports knowledge graph fusion by combining
the characteristics of sports knowledge graph, and the framework structure of sports knowledge graph
fusion is shown in Figure 1.

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KNOWLEDGE GRAPH CONSTRUCTION FOR INTELLIGENT PHYSICAL

Data
preprocessing

Knowledge
mapping

Redundancy
removal

Knowledge Graph
Approximation

Similarity
calculation

Vectorized
representation

Similarity
Search

Calculation

Knowledge
Fusion

Semantic
Information

Structural
Information

Integration of
knowledge

Knowledge
Fusion

Fig. 1. The integration architecture of the physical knowledge atlas

This paper proposes a framework structure for knowledge fusion in sports, whose framework includes
three parts data preprocessing, similarity calculation, and knowledge fusion. Among them, data
preprocessing is actually the approximation of the knowledge triples obtained in Section III to remove
redundant data. In addition, in order to obtain more effective knowledge subgraphs, the similarity
algorithm of graphs is utilized to compute the knowledge. Finally, the fusion of knowledge is obtained by
using the double adjacency matrix.
2.4.2. Sports Knowledge Graph Approximation. The knowledge graph obtained in this paper has a
certain degree of redundancy. Therefore, this section performs the approximation process for each
knowledge graph, that is, the knowledge sub-graph set of the sport, by considering the knowledge nodes
of the sport knowledge graph as individual vertices, and the most central feature of the adopted method is
to use the vertex weight information and the proportion of the association weight information between
individual vertices to carry out the realization.
In this paper, firstly, in order to get the number of vertices and edges in each knowledge subgraph, then
the number of vertices is denoted as VG and the number of edges is denoted as EG according to the
size of the vertices and related data in each knowledge subgraph Gk . Secondly, the weight information of
the vertices and edges is calculated accordingly. Finally, the corresponding knowledge subgraphs are
filtered according to the size of the calculated weight information, which is calculated by the following
formula.

n
 Tvi , vi  vG
T  Gk ,Vi    i 1
 0, v  v
i
G


(13)

Where, the knowledge subgraph is denoted as Gk , and the sum of the weight information of each vertex
in the knowledge subgraph is denoted as Tv  Gk ,Vi  . Where Vi denotes the information of the i th
vertex, and Tvi

denotes the weight information of the i th vertex, and n denotes the number of vertices

in the knowledge subgraph, which is denoted as VG in this paper.
In addition to this, the formula for calculating the weight information of the edges is shown below:
1,  vi , v j   EG

TE  Gk , vi , v j   

0,  vi , v j   EG

(14)

where TE  Gk ,Vi ,V j  indicates whether there are edges interconnected with both Vi and V j in a

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knowledge subgraph, respectively, which can be represented by 1 if there are, and 0 otherwise.

TE  Gk ,Vi ,V j    TE Vi ,V j  *TE  Gk ,Vi ,V j 
n

n

i 1

j

(15)

Where T Vi , V j  represents the weight information of the edges in each knowledge subgraph, and n
represents the number of vertices in each knowledge subgraph.
Ultimately, this paper utilizes the designed algorithms to realize the processing of knowledge subgraphs
by using the weight information values of vertices and edges in each knowledge subgraph that have been
obtained.
2.4.3. Similarity calculation of sports knowledge subgraphs. Sports knowledge graph is ultimately
composed of sets of individual knowledge subgraphs, in which their associated edges and vertices have
similar semantic information. Based on this, this paper proposes a similarity calculation method to realize
the fusion of sports knowledge subgraphs. Firstly, in order to get the candidate set, this paper constructs
a bipartite graph according to the distance between its individual graphs, and obtains it by using similarity
calculation; secondly, this paper calculates the semantic information carried by each vertex by using cosine
similarity to obtain the corresponding results.
In order to calculate the similarity of knowledge subgraphs more accurately, this paper also utilizes the
information carried by the vertices in each knowledge subgraph set for judgment. Therefore, this paper
proposes to utilize the cosine similarity to get the similarity between its vertices, and the specific calculation
is shown below. Where “*” denotes the vector product, a and b denote two vectors, and  denotes the
angle between two vectors.

cos  

a *b
ab

(16)

2.4.4. Sports Knowledge Subgraph Fusion. In the process of constructing sports knowledge map, in
order to construct sports knowledge map more accurately, it is necessary to carry out the fusion operation
for this knowledge. In this paper, a fusion method of sports knowledge graph is proposed, and its specific
fusion process mainly includes the following five steps.
1) Representation of the weight matrix of the corresponding vertices in the sports knowledge graph. In
order to better realize the overall fusion of sports knowledge graph, firstly, the weight information between
each sport is calculated, which is represented as TE  Gk ,Vi ,V j  , and then all the sports knowledge
subgraphs are converted into a weight matrix, which is represented as En * n   E1 , E2 ,

TE ,  ei , j  Gk   V j VG 
Ek i  j  
0, ei , j  Gk


, En  .

(17)

In this paper, we use TE to denote the association weight information of each edge of a sports
knowledge subgraph, and Ek to denote whether there is a corresponding edge connected between each
knowledge point in a sports knowledge sub, and if there is, it is denoted as TE ; otherwise, the matrix is
defined as 0.
2) Weight matrix representation of semantic information of each vertex. Next, this paper represents the
, Mn 
semantic information of each vertex, that is, the labeling information, as a matrix M   M1 , M 2 ,
as shown in the following equation.
M k [i][1]  Ti ,Vi  Gk

(18)

Where M k i 1 denotes the information in the sports knowledge subgraph is stored accordingly
using the matrix of n *1 . And the corresponding Ti represents the semantic information carried by each

KNOWLEDGE GRAPH CONSTRUCTION FOR INTELLIGENT PHYSICAL

2187

vertex in the sports knowledge subgraph.
3) Similarity Calculation of Semantic Similarity Matrix. Further, this paper proposes to utilize the
similarity function for solving SimsT  Gkli , Gkij  , which can be realized to convert the textual information
into the semantic similarity matrix of sports knowledge.
n

cos  a, b  

a b
k 1

n

k k
n

a b
2

k 1

k 1

(19)

2
k

M i  j   M k i  j   M kTk  j 1  SimsT  Gklj , Gklj 

(20)

where M i  j  represents the similarity matrices in the labels Gklj and Gklj of the two vertices,
respectively, and where * represents the similarity computation between the two matrices.
4) Matrix Fusion. Secondly, this paper sums the matrices formed by the knowledge subgraphs Ek to
obtain the final fusion matrix E1,2,3, ,n . Where n denotes the number of subgraphs, and E1,2,3, ,n denotes
the result of fusing the n knowledge subgraphs.

 1
 2  E1  E1  , n  2
E1,2,3, ,n 
1 E
 En  , n  3
 2 1,2,3, ,n

(21)

5) Matrix update. Eventually, in order to obtain the fusion matrix EMk , it is necessary to combine the
conceptual information of each vertex to realize the update of the matrix.
2.5.

Storage and visualization of sports knowledge graphs

In this paper, the neo4j community version was chosen to implement the storage of the computer network
course ontology. The nodes and interrelationships of the Sports Knowledge Graph are usually stored in a
graph database, and then the data about the Sports Knowledge Graph is collected and queried using the
data structure of “graph”. Therefore, in this paper, we save the generated ontology of the university
computer network in csv file format, and then save the ontology in the neo4j graph database according to
the csv file import syntax provided by neo4j. In this paper, the constructed knowledge graph is stored
using neo4j and visualization is done using D3 library.

3. Sports recommendation model based on knowledge graph and neural network
3.1. Neural Networks
In neural network models, one of the most commonly used models is single hidden layer feedforward neural
networks (SLFNs), which are typically exemplified by multilayer perceptrons (MLPs), BP neural networks,
etc. The number of nodes of the input layer in SLFNs is basically the same as the dimensions of the sample
feature space; therefore, the samples are often preprocessed prior to the learning of the network, and the
conventional approach is to normalize and Dimensionality reduction. Standardization can make the
sampling mean close to 0, and make the sample variance value is basically the same, which can statistically
accelerate the learning speed of students. When the dimensionality of the features is high, the data may
contain redundant and noisy information, and the model training will take a lot of time; moreover, in
practical applications, it will bring errors to the model identification, so that the correct rate of the model
decreases, and the use of the feature dimensionality reduction method can reduce the errors caused by
redundant information, which in turn improves the correct rate of the model.
In the context of knowledge graph, semantic information such as images and text need to be represented

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as low-dimensional dense entity vectors, i.e., knowledge representation learning, and then combined with
neural networks to train and compute the model.
3.2. Recommendation model based on knowledge graph and neural network
The author proposes a sports training program recommendation model based on knowledge graph and
neural network, which contains 4 parts.
1) Input of vectors. Sparse prioritized vectors from students and courses are taken as inputs and converted
into expressions of low-dimensional density vectors.
2) Deep feature learning. Taking the learner vector and course vector Embedding as inputs, the MLP is
used to mine the features of the learner and the course to obtain a nonlinear feature representation between
the learner and the course.The MLP module uses ReLu as the activation function, i.e., f  x   max  0, x  .
The input vector of the neuron from the previous level of the neural network is passed to the neuron x ,
and the neuron that uses ReLu to activate the function will output max  0, W T x  b  , to the neuron at the
next level, or output to the whole neuron. In the above equation, W is the weight matrix whose element
wij represents the weight between the i th neuron in the hidden layer and the j th neuron in the input
layer, and b is the bias term. The final output of the MLP is denoted as  M .
3) Learning of knowledge graph properties. On this basis, an approach based on multi-level relationship
diffusion is proposed. By learning the entity-relationship vectors in the knowledge graph, the linear
properties of students and courses can be understood. The set of all courses selected by a student t is
Kl  k1 , k2 , , kn  , and the set of entities contained in course names in K t is Et  e1 , e2 , , e  . K t is
used as the seed set of the knowledge graph. In the knowledge graph, the seed entities will be propagated
outward along the correlation r , and the set of 3 elements contained in each propagation, which is the set
of triples whose distance from the seed set is d , is Std (d  1, 2, , h) . Std will be interacted with the
course subjunctive entities several times to obtain the student's preference for the course. All the preference
h

values are combined to derive the student's feature TR   Ot , where Eq. Otd denotes the student t ’s
d

d 1

interest feature at level d . The final output of the knowledge graph feature learning module is:
 R  TR K R

(22)

Where:  represents the inner product calculation; K R describes the instructional characteristics of
each course.
4) Connection prediction. First, the vectors learned by the deep feature learning module and knowledge
graph features are linearized, then they are speciﬁed by the Sigmoid function, and finally the desired
prediction is calculated:

  
ytc     T  R  
 M  


(23)

x
Where:  ( x)  1/ 1  e  ;  T is a vector of weights obtained by training the model; ytc denotes the
probability predicted by the model that a trainee t chooses a course c .

3.3. Similarity Fusion of Collaborative Filtering and Knowledge Graph Recommendation Results
Although the collaborative filtering method has high recommendation accuracy, it lacks support in
semantic similarity. Knowledge graph enhances the semantic interpretability of the recommendation
results, based on the semantic recommendation model obtained by feature learning, and then combined
with the collaborative filtering idea to linearly weighted fusion of the 2 recommendation models, the fusion
method is shown in Figure 2.

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KNOWLEDGE GRAPH CONSTRUCTION FOR INTELLIGENT PHYSICAL

Course Selection
Information

Data Cleaning

Knowledge
Extraction

Knowledge Graph

Grading Matrix

Vector
Representation

Collaborative
filtering similarity

Semantic similarity

Semantic perspective

Preferred Angle

Grading Data

Similarity
Association Fusion

Recommendation
results

Fig. 2. Fusion algorithm computing process

Integration methods for:
R(t , c)    tk  (1   )  yk

(24)

Where:  is the weight coefficient, and 0    1 , can be obtained after training; R(t , c) is the tuning
probability of recommending the course c to the user t after the fusion algorithm.

4. Extracted results of sports knowledge
4.1. Entity extraction results
The experimental data is a self-constructed dataset of junior high school mathematics knowledge entities
made by manual tagging and semi-automatic tagging in this paper, with a total of 9600 corpora. The
preprocessed corpus is randomly disrupted and divided into three datasets, namely, training, validation
and testing, according to 8:1:1.
On the self-constructed dataset, HMM, CRF, BiLSTM, BERT-softmax, BERT-CRF, and the BERT-BiLSTM-CRF model used in this paper were used to do the comparison experiments. The experimental
results are shown in Table 1 and Table 2.
Table 1 and Table 2 show the recognition results of the sports objective existence entity and the
recognition results of the sports logical concept entity, respectively. The BiLSTM-CRF model is used for
sports information recognition, and after the Chinese text is vectorized using word-by-word quantization,
the bidirectional long and short-term memory network is used to obtain the bidirectional semantic features
of the front and back texts. The experimental results show that the BERT-BiLSTM-CRF model has a
better recognition effect than the traditional named entity recognition models such as HMM, CRF and
BiLSTM. In the recognition effect of sports objective existence entity and the recognition result of sports
logical concept entity, its precision rate, recall rate and F1 value are higher than the other four models, and
the recognition precision rate of sports objective existence entity is optimized by 1.45% than the BERTCRF model. Therefore, this paper chooses to use BiLSTM-CRF as the model for named entity recognition

2190

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under the consideration of its own experimental equipment and other factors.
Table 1. Shows the identification result of the existence of the entity
Method

Accuracy rate/%

Recall rate/%

F1 score/%

HMM

82.06

83.61

83.33

CRF

86.87

89.7

88.28

BiLSTM

95.14

96.46

95.31

BERT-softmax

38.34

59.28

46.58

BERT-CRF

96.39

95.77

96.06

BERT-BiLSTM-CRF

97.79

96.76

96.56

Table 2. Logical conceptual entity recognition results
Method

Accuracy rate/%

Recall rate/%

F1 score/%

HMM

82.06

83.58

83.32

CRF

86.86

89.67

88.26

BiLSTM

95.15

96.43

95.36

BERT-softmax

38.37

59.3

46.58

BERT-CRF

96.39

95.73

96.04

BERT-BiLSTM-CRF

96.54

96.75

96.58

4.2. Relationship extraction results
By fine-tuning AGGN on the training set with the BERT model on the training set and preserving its
optimal weights on the validation set, this paper compares the F1 scores on the test set, and the comparison
results are shown in Table 3.AGGCN achieves good results, but when compared with the BERT model,
both in terms of the F1 scores as well as in terms of the precision rate and the recall rate, the results of the
BERT model are relatively better,and its performance on all three metrics is about one percentage point
higher than that of AGGCN.
Table 3. Results of the method of the parties
Method

Accuracy rate/%

Recall rate/%

F1 score/%

AGGCN

82.69

82.46

82.15

BERT

83.59

85.67

85.98

5. Knowledge mapping example extraction results
The knowledge graph extracts a total of 2700 instances and 25412 relations from the structured dataset
PAHF. The results of knowledge graph instance extraction and knowledge graph association extraction
are shown in Table 4 and Table 5, respectively.

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KNOWLEDGE GRAPH CONSTRUCTION FOR INTELLIGENT PHYSICAL

Table 4. The PAHF knowledge map instance extracts the results
Name

Data volume

Cassification

Idividuals

1008

Individual information

Grade

10

Individual information

Bmi

10

Individual information

Age

10

Individual information

Gender

5

Individual information

Region

30

Individual information

Race

15

Individual information

Sports plan

368

Motion information

Motion frequency

55

Motion information

Type of motion

7

Motion information

Motor intensity

5

Motion information

Motion length

82

Motion information

Mode of motion

25

Motion information

Outcome indicator

320

Motion information

Correlation study

385

Evidence information

bibliography

385

Evidence information

Total

2720
Table 5. PAHF knowledge mapping results

Name

Domain

Range

Cassification

Grade
Age
Belong

Individuals

Gender
Race

11562

Region
BMI
Intervention

Sports plan

Individuals

523

Motion frequency
Motor intensity
Include

Sports plan

Motion length

3568

Type of motion
Mode of motion
Intervention result

Individuals

Outcome indicator

1253

Evidence support

Bibliography

Sports plan

539

source

Reference literature

bibliography

346

Total

17791

The user interaction module of the recommender system mainly includes user information collection,
sports program recommendation and related knowledge graph visualization. First, the user inputs
individual characteristic data in the information collection module. After the collection is completed, the
recommendation module matches the existing data in the knowledge graph according to the user
characteristic data, and calculates the similarity coefficient between the user characteristics and the
“individual” nodes in the knowledge graph, and selects the sports programs associated with the nodes that
perfectly match as the main recommendation results, and the sports programs associated with the
“individual” nodes with the user characteristics whose similarity coefficients are greater than 0.95 as the
main recommendation results. The sports programs associated with “individual” nodes with similarity

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coefficients greater than 0.95 with user characteristics are used as extended recommendation results.
Finally, the recommendation module returns the relevant nodes and relationship data of the sports
programs to the visualization module for display. The module also provides result view switching, data
filtering and comparison, and recommendation result downloading functions to help users flexibly compare
and analyze the recommendation results.
Take the input user characteristics (Individual.id=832) as an example. The recommender system
recommends a total of 2 examples of sports programs, including 2 types of exercise frequency, 2 types of
exercise intensity, 3 types of exercise duration and 4 types of exercise for the user to choose from, and the
recommendation results of the personalized sports program recommender system are visualized as shown
in Figure 3. Recommendations are derived from three evidence-supported “individual” nodes with feature
similarity scores to the user node of 0.994 1 (Individual.id=2075), 0.9888 (Individu-al.id=1941), and 0.9880
(Individual.id=1943). The "Individual" nodes "1943" and "1941" and their related sports programs are
derived from the same study, and the "Individual" node "2075" is derived from another study.

Fig. 3. The personalized sports plan recommends results visualization

6. Effectiveness of pedagogical applications
The experimental subjects selected for this study are the first year students of a sports college, before
conducting the experiment on the experimental group of this paper sports training program and the
control group of MICT data pre-tests for statistics, the resulting data for Spss data pre-tests for
independent samples T-test. The results of the test showed that in conducting the test of variance, the sig
coefficients are more than 0.05, which can be reflected in the data is very small, the variance chi-square
data is established, and you can begin to test the parameters. The results of the measured data were
subjected to independent samples T-test, and the test results showed that the sig coefficients were all
greater than 0.05. The final result was obtained that there was no significant difference between the two
groups of data pre-test, which proved that there was no big difference between the two groups of subjects
in the level of physical fitness of the students, which could be used for the experiment, and did not affect
the results and conclusions of the experiment. Table 6 and Table 7 show the paired samples T-test results
of the cardiorespiratory fitness performance of the two groups of female students before and after the

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KNOWLEDGE GRAPH CONSTRUCTION FOR INTELLIGENT PHYSICAL

intervention of the training modality established in this paper and the results of the independent samples
T-test of the two groups of female students after the exercise intervention, and Table 8 and Table 9 show
the paired samples T-test results of the 1000m running and spirometry performance of the two groups of
male students before and after the intervention of the training modality and the results of the independent
samples T-test of the two male students after intervention of the 1000m and spirometry.
In this paper, before and after the intervention of the physical training program group of female subjects
in the 800-meter running experiment by paired samples T-test P=0.000<0.05. The lung capacity of the
female subjects was increased from 2581±226 before the intervention to 2725±238 after the intervention
by calculating the mean value of the increase of 144 ml before and after the intervention, and before and
after the intervention data were subjected to a paired samples T-test P=0.000<0.05.
Paired samples t-test of the data before and after the intervention of 1000 meter run of the subjects' boys
yielded P=0.000<0.05. Lung capacity of the subjects' boys improved from 3139±278 to 3355±227 before
the intervention. A mean increase of 216 ml before and after the intervention was calculated, and the data
before and after the intervention were subjected to a paired samples t-test, p=0.000<0.05. In summary, we
can find that for the improvement aspect of cardiorespiratory fitness in adolescent students, the 8-week
training intervention of this paper's physical training program can be improved to a great extent.
Paired samples t-test for spirometry data of both male and female students before and after the
intervention p-value was less than 0.05. In conclusion, it can be concluded that 8 weeks of MICT
intervention to improve cardiorespiratory fitness of the students was equally significant.
Independent samples t-test was conducted on the performance of girls' experimental and control groups
after the intervention, and the results showed that P=0.039<0.05 for the girls' 800m running performance,
and P=0.316>0.05 for the girls' spirometry performance, and it can be concluded that there was a
significant difference between the two training modalities in terms of improvement of the 800m
performance after 8 weeks of the training intervention but there was not a significant difference between
the two training modalities in terms of improvement of the girls' However, there was no significant
difference in improving the lung capacity of the girls.
Independent samples t-test on the performance of the boys' experimental and control groups after the
intervention showed that the results of the analysis of the boys' performance in the 1000 meter run
P=0.315>0.05, P=0.433>0.05 for the boys' spirometry performance, it can be concluded that i.e., after 8
weeks of training intervention the two training modalities do not have any significant difference.
However, by analyzing and comparing the improvement of girls' 800-meter running and lung capacity
performance before and after the intervention, the improvement of 800-meter running performance of the
experimental group in the physical training program group of this paper was better than that of the control
group of MICT, and the performance of the students under the physical training program of this paper
was improved by 0.17 min, which was more than that of the improvement of MICT by 0.13 min. By
analyzing and comparing the improvement of 1000m running and lung capacity performance of the
subjects before and after the intervention, the improvement of the performance of 1000m running in the
experimental group of the physical training program group of this paper was also better than that of the
control group MICT.
Table 6. Cardiopulmonary function of the girl before and after the intervention
800 meters run (n=30)
Index

Training methods
of this article

Before the
experiment

Lung activity (n=30)

MIC

Training methods
of this article

MIC

3.92±0.16

3.95±0.25

2581±226

2613±225

After the
experiment

3.75±0.17

3.91±0.25

2725±238

2642±233

T test

0.000

0.024

0.000

0.008

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H. LI

Table 7. The two groups of girls were tested for independent sample t
800 meters run (n=30)
Index
After the
experiment

Lung activity (n=30)

Training methods
of this article

MIC

Training methods
of this article

MIC

3.75±0.17

3.91±0.25

2725±238

2642±233

T test

0.035

0.311

Table 8. Cardiopulmonary function of the boys before and after the intervention
1000 meters run (n=30)
Index

Lung activity (n=30)

Training methods
of this article

MIC

Training methods
of this article

MIC

Before the
experiment

4.15±0.23

4.15±0.35

3139±278

3074±266

After the
experiment

3.82±0.18

4.11±0.39

3355±227

3374±297

T test

0.000

0.025

0.000

0.008

Table 9. The two groups of boys were tested for independent sample t
1000 meters run (n=30)
Index
After the
experiment
T test

Lung activity (n=30)

Training methods
of this article

MIC

Training methods
of this article

MIC

3.82±0.18

4.11±0.39

3355±227

3374±297

0.315

0.433

Table 10 shows the comparison of the data of the students in the seated forward bending program before
and after the 8-week intervention of this sports training program and MICT, after the 8-week training
intervention, the seated forward bending performance of the male subjects in the experimental group of
this sports training program improved, and the paired samples t-test was conducted before and after the
intervention, and sig=0.025<0.05. The seated forward bending performance of female subjects also
improved to some extent. There was also an improvement in the female subjects' sitting forward bending
performance, as shown by comparing the pre- and post-intervention data and conducting a paired samples
t-test, p=0.001<0.05. Based on this, 8 weeks of training with the sports training program of this paper can
greatly improve the students' performance in sitting forward bending program.
MICT control group of subjects male students sitting forward bending test data before and after the
intervention for paired samples t-test p=0.036<0.05 and subjects female students sitting forward bending
data before and after the intervention for paired samples t-test p=0.017<0.05. Thus it can be concluded
that conducting MICT training for a period of 8 weeks can have a significant effect on the performance of
sitting forward bending. Comparison of the paired samples t-test data shows that both modalities can
improve the students' performance in this event, with the physical training program in this paper
improving more.
Table 11 shows that the results of the experimental group and the control group after intervention are
independently sampled T-test, and the results show that the results of sitting forward bending analysis are
P=0.785>0.05 for boys and P=0.692>0.05 for girls, and it can be concluded that after 8 weeks of training
intervention, there is no significant difference between the two training methods in terms of flexibility, but
the method in this paper plays a good role in improving flexibility.
Table 10. Test of the t test of precursors before and after intervention

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KNOWLEDGE GRAPH CONSTRUCTION FOR INTELLIGENT PHYSICAL

Predisposition (male n= 30)
Index

Predisposition (female n= 30)

Training methods
of this article

MIC

Training methods
of this article

MIC

6.11±4.74

6.33±4.04

9.45±4.16

9.54±3.29

After the
experiment

6.63±4.39

6.85±4.24

10.04±3.86

9.72±2.97

T test

0.025

0.036

0.001

0.017

Before the
experiment

Table 11. The two groups of the seated position of the seat were tested
Predisposition (male n= 30)
Index
After the
experiment
T test

Predisposition (female n= 30)

Training methods
of this article

MIC

Training methods
of this article

MIC

6.63±4.39

6.85±4.24

10.04±3.86

9.72±2.97

0.785

0.692

7. Conclusion
This paper realizes the construction of sports knowledge graph, which is constructed to provide support
for sports intelligent assisted teaching.
1) The sports knowledge graph outperforms the other four comparative models on both entities, and the
recognition accuracy is optimized by 1.45% on the sports objective existence entity.AGGN is compared
with the BERT model of this paper on the training set. This paper's relationship extraction model
outperforms AGGN by about one percentage point on all three metrics. So this paper chooses to use
BiLSTM-CRF as the model for named entity recognition and selects BERT as the model for relationship
extraction is more scientific.
2) This paper sports training program group experimental group 800 m running performance and 1000 m
running performance enhancement are better than the control group MICT, this paper training program
under the students 800 m running performance improvement than MICT enhancement of 0.13min more
than the MICT, which explains the effect of this paper's training methods on the enhancement of students'
cardiorespiratory capacity is better than the MICT sports training methods. The results of independent
samples t-test of the performance of the experimental group and the control group after the intervention
showed that the P value of seated forward bending of male students was 0.785, and the P value of female
students was 0.692 were greater than 0.05, so it can be concluded that the difference between the two
training methods in terms of the students' flexibility qualities is not significant, but the method of this
paper plays a better role for the improvement of the students' flexibility qualities, which proves the
effectiveness of the sports course recommendation model based on the knowledge graph with the Neural
network-based sports course recommendation model can play a better role in assisting students' training.

References
[1]

Rajšp, A., & Fister Jr, I. (2020). A systematic literature review of intelligent data analysis methods for smart
sport training. Applied Sciences, 10(9), 3013.

[2]

Bhatia, M. (2021). Intelligent system of game-theory-based decision making in smart sports industry. ACM
Transactions on Intelligent Systems and Technology (TIST), 12(3), 1-23.

[3]

Alhadad, S. A., & Abood, O. G. (2018). Enhancing smart sport management based on information technology.
Int Organiz, 5, 19-26.

2196

H. LI

[4]

Ha, J. P., Kang, S. J., & Kim, Y. (2017). Sport fans in a “smart sport”(SS) age: Drivers of smartphone use for
sport consumption. International Journal of Sports Marketing and Sponsorship, 18(3), 281-297.

[5]

Guo, S. (2022). Sports Smart Data Writing Based on New‐Type Semiconductor Nonvolatile Storage Mode.
Scientific Programming, 2022(1), 2770333.

[6]

Petrie, G., & Toth, C. K. (2018). Introduction to laser ranging, profiling, and scanning. In Topographic laser
ranging and scanning (pp. 1-28). CRC Press.

[7]

Zhang, L., Jiang, S., & Shi, G. X. (2024). Intelligent Learning-Driven College Physical Education Teaching
Method based on Fuzzy Evaluation Theory. Computer-Aided Design and Applications, 21, 186-204.

[8]

Da-Wei, C., Chao, L., Shun, W., Xun-Ling, W., & Wen-fang, W. (2018). Research and application of
multimedia digital platform in the teaching of college physical education course. Journal of Intelligent & Fuzzy
Systems, 34(2), 893-901.

[9]

Odeh, A. Y., Shabib, S. S., Ghazi, M. A., & Hassan, L. (2024). Developing physical education curricula in the
age of artificial intelligence. Journal of Studies and Researches of Sport Education, 34(3).

[10] Jing, Z., & Chen, L. (2021). Research on the innovative development of intelligent physical education teaching
in universities. International Journal of Higher Education Teaching Theory, 2(4), 1093-1098.
[11] Cao, Z. (2021, April). Research on Sports Computer Integrated Management System Based on Network
Environment under the Background of Smart Sports. In 2021 IEEE Asia-Pacific Conference on Image
Processing, Electronics and Computers (IPEC) (pp. 1108-1111). IEEE.
[12] Erazo, I. (2023, December). Smart Sports Predictions via Hybrid Simulation: NBA Case Study. In 2023 Winter
Simulation Conference (WSC) (pp. 1375-1386). IEEE.
[13] Deng, C., Yu, Q., & Luo, G. (2022). Construction of Smart Sports in Colleges and Universities: Influencing
Factors, Design Ideas, and Model Choices. Mobile Information Systems, 2022(1), 9041042.
[14] Xu, J., Kim, S., Song, M., Jeong, M., Kim, D., Kang, J., ... & Ding, Y. (2020). Building a PubMed knowledge
graph. Scientific data, 7(1), 205.
[15] Ji, S., Pan, S., Cambria, E., Marttinen, P., & Philip, S. Y. (2021). A survey on knowledge graphs: Representation,
acquisition, and applications. IEEE transactions on neural networks and learning systems, 33(2), 494-514.
[16] Zou, X. (2020, March). A survey on application of knowledge graph. In Journal of Physics: Conference Series
(Vol. 1487, No. 1, p. 012016). IOP Publishing.
[17] Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., Melo, G. D., Gutierrez, C., ... & Zimmermann, A. (2021).
Knowledge graphs. ACM Computing Surveys (Csur), 54(4), 1-37.
[18] Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017). Knowledge graph embedding: A survey of approaches and
applications. IEEE transactions on knowledge and data engineering, 29(12), 2724-2743.
[19] Shi, B., & Weninger, T. (2018, April). Open-world knowledge graph completion. In Proceedings of the AAAI
conference on artificial intelligence (Vol. 32, No. 1).
[20] Wang, H., Zhang, F., Zhao, M., Li, W., Xie, X., & Guo, M. (2019, May). Multi-task feature learning for
knowledge graph enhanced recommendation. In The world wide web conference (pp. 2000-2010).
[21] Huang, X., Zhang, J., Li, D., & Li, P. (2019, January). Knowledge graph embedding based question answering.
In Proceedings of the twelfth ACM international conference on web search and data mining (pp. 105-113).
[22] Tian, L., Zhou, X., Wu, Y. P., Zhou, W. T., Zhang, J. H., & Zhang, T. S. (2022). Knowledge graph and
knowledge reasoning: A systematic review. Journal of Electronic Science and Technology, 20(2), 100159.
[23] Bader, S. R., Grangel-Gonzalez, I., Nanjappa, P., Vidal, M. E., & Maleshkova, M. (2020). A knowledge graph
for industry 4.0. In The Semantic Web: 17th International Conference, ESWC 2020, Heraklion, Crete, Greece,

KNOWLEDGE GRAPH CONSTRUCTION FOR INTELLIGENT PHYSICAL

2197

May 31–June 4, 2020, Proceedings 17 (pp. 465-480). Springer International Publishing.
[24] Zamini, M., Reza, H., & Rabiei, M. (2022). A review of knowledge graph completion. Information, 13(8), 396.
[25] Pengwei Pan,Jingpei Lei,Jiaan Wang,Dantong Ouyang,Jianfeng Qu & Zhixu Li. (2025). Concept-aware
embedding for logical query reasoning over knowledge graphs. Information Processing and
Management(2),103971-103971.
[26] Yingbo Xie,Junfei Qiao & Ding Wang. (2025). A weighted knowledge extraction strategy for dynamic multiobjective optimization. Swarm and Evolutionary Computation101773-101773.
[27] Cai Buqing,Tian Shengwei,Yu Long,Long Jun,Zhou Tiejun & Wang Bo. (2024). ATBBC: Named entity
recognition in emergency domains based on joint BERT-BILSTM-CRF adversarial training. Journal of
Intelligent & Fuzzy Systems(2),4063-4076.
[28] Arar Al Tawil,Laiali Almazaydeh,Doaa Qawasmeh,Baraah Qawasmeh,Mohammad Alshinwan & Khaled
Elleithy. (2024). Comparative Analysis of Machine Learning Algorithms for Email Phishing Detection Using
TF-IDF, Word2Vec, and BERT. Computers, Materials & Continua(2),3395-3412.
</reference>

<statements>
1. Video and motion capture for posture and action recognition.
2. Cameras (RGB or depth) and motion capture track body posture and movement; microphones capture verbal instructions and feedback.
3. Employ CNN/ViT + GCN for video-based action recognition, mapping 2D/3D keypoints to skeletal graphs for biomechanical insight, as in athlete motion recognition systems and Xi’s Transformer–GCN hybrid.
4. Improved recognition and diagnosis: Action recognition accuracies above 95% and anomaly detection accuracies around 98% are achievable with Transformer-based and hybrid fusion models, enabling fine-grained detection of technique errors and unsafe workload patterns.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.