# Prediction Analysis of College Students’ Physical Activity Behavior by Improving Gray Wolf Algorithm and Support Vector Machine

Authors: Minjian Wang
Journal: Advances in Multimedia
 (2022)

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## Abstract

Physical exercise for college students is an important means to build a healthy standard of college students and an important way to a healthy campus. In addition to creating good physical fitness, physical exercise has significant effects on improving psychological stress and alleviating psychological problems and mental illnesses among college students. It is important to predict and analyze the physical exercise behavior of college students and explore the positive value of physical exercise for college education. In order to overcome the problem of low accuracy of traditional algorithms in prediction, this paper uses the improved gray wolf algorithm (IGWO) and support vector machine (SVM) for predictive analysis of college students' physical exercise behavior. A nonlinear decreasing convergence factor strategy and an inertia weight strategy are introduced to improve the gray wolf optimization algorithm, which is used to determine the SVM parameters for the purpose of improving the model accuracy. Then, the college students' physical exercise data are input into the model for validation. By constructing a campus behavior data set of college students and conducting experiments, the algorithm achieves 90.45% behavior prediction accuracy, which is better than that of typical prediction models. Finally, individual growth monitoring of college students is targeted to warn students with abnormal behaviors. At the same time, the higher-order information such as physical exercise behavior habits of college students is explored to provide meaningful reference for constructing personalized training.


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## Body

## 1. Introduction

Promoting physical exercise among college students is an important part of the national health strategy and also one of the mainstream directions of current research [1]. In the field of college students' physical exercise psychology, physical exercise intention and physical exercise behavior are two common basic concepts [2]. Most researchers regard physical exercise intention as a key factor to promote physical exercise behavior, and some even use physical exercise intention as a dependent variable in physical exercise promotion studies [3]. But studies of the consistency of intention and behavior have shown that there is a gulf between intention and behavior. Therefore, the question to be answered in this study is how physical exercise intention can predict physical exercise behavior.Exercise intention is people's decision to take a physical exercise behavior [4]. People can form a behavior or goal intention by saying “I plan to do a certain sport,” “I intend to do a certain sport,” “I will do a certain sport,” etc. This is the result of deliberate consideration of what the individual is going to do and can indicate the level of effort that the individual will put in to achieve the desired result. Physical exercise intention was regarded as the proximal determinant of physical exercise behavior by researchers [5]. For example, when using rational behavior theory, planned behavior theory, attitude and behavior theory, and protective motivation theory to study the change of physical exercise behavior, they all agree that the most direct and important predictor of individual physical exercise behavior is individual physical exercise intention.The analysis of college students' physical exercise behavior, especially the spatiotemporal modeling of campus data to mine the physical exercise behavior pattern, is a current research hot spot in the interdisciplinary disciplines of computer science, behavioral psychology, and education [6]. As one of the most concerned groups in the whole society, college students have more and more mental health problems [7]. The analysis of physical exercise behaviors and patterns of college students is one of the important topics dealt with in university education. It is an important guideline for college students to regulate their own study and life, for colleges and universities to make teaching plans and regulations, and for the state to make guidelines and policies of higher education [8]. The prediction of college students' physical exercise behavior has important theoretical and application values in the aspects of students' physical health control, mental health education, abnormal behavior detection, education, and talent cultivation. The intelligent education mode supported by information technology has become the development trend of modern education informatization [9]. The physical exercise behaviors of college students are continuously recorded by various devices, forming a large amount of multisource fusion data reflecting students' physical health information. The physical activity data of college students are highly nonlinear and correlated [10]. By extracting the activity characteristics, analyzing the correlations, and mining the spatiotemporal process of their activity habits, we can predict the physical activity behavior and reason about the abnormal behavior on campus.The prediction of college students' physical activity behavior is a typical problem of spatiotemporal data prediction [11]. On the one hand, the campus data record students' daily physical exercise activities by time. On the other hand, from the trajectory of students' physical exercise activities, the trajectory data has strong semantic information, reflecting the trajectory location association of different physical exercise activities in a certain time series [12]. Therefore, it is helpful to improve the accuracy of spatiotemporal behavior prediction by fully considering the connectivity, time series, and semantic meaning among trajectory locations. Figure 1 shows the physical exercise behavior data of college students expressed in time slices according to spatiotemporal correlation. In the temporal dimension, the content of physical exercise behaviors of college students at different moments will have a certain influence on the physical exercise behaviors of that node in future moments. In the spatial dimension, different location nodes in the campus road network have strong semantic relationships and a series of mutual influences between nodes. At the same time, location nodes will also have influence on their associated nodes at different moments. It can be seen that college students' physical exercise activities have strong correlations in both temporal and spatial dimensions, and different location points have unique semantic association relationships with each other [13]. It is a great challenge to mine behavioral characteristics from these complex, nonlinear, and semantic spatiotemporal data to achieve behavioral prediction.Figure 1
Diagram of spatial and temporal correlation of campus daily behavior data.Foreign scholars have also conducted empirical studies on the relationship between the two. In the literature [14], it has been shown that the duration and frequency of exercise are negatively associated with the level of anxiety and depression in college students and that the level of anxiety and depression decreases as the duration of physical activity increases. In a clinical trial, the literature [15] showed that exercise-based treatment can provide relief for patients with severe or mild depression. The literature [16] pointed out that whether or not to participate in physical activity and the frequency of participation in it will cause a significant effect on the level of psychological subhealth of our population. The literature [17] pointed out that gray system model does not map well the complex nonlinearity of physical activity behavior. Literature [18] pointed out that Elman suffers from the problem of randomly determining weights and thresholds and requires enough training data to predict physical activity behaviors well. For short data and information-poor physical exercise behavior data, support vector machine (SVM) is chosen for prediction [19].To address the above problems, in order to better analyze the physical exercise behavior prediction of college students, this paper first defines the hierarchical mathematical relationships between physical exercise activities, behaviors, and behavior prediction of college students. By combining the strong semantic relationships and spatiotemporal correlations among behavioral data locations, semantic road networks and specific cycle segments are constructed. Then, a nonlinear decreasing convergence factor and an inertia weighting strategy based on Euclidean distance are introduced to improve the basic gray wolf algorithm. An improved gray wolf algorithm (IGWO) and support vector machine (SVM) are used to build a prediction model of college students' physical activity. The graph structure representation of college students' physical activity is established, and the output of multiple time segments is convolved and fused to obtain the behavior prediction results. Finally, we collected and constructed a data set of college students' physical activity behaviors, analyzed the behavioral prediction experiment results, identified the abnormal behaviors of college students on campus, and mined the information of personal activity habits.The innovations and contributions of this paper are listed below.(1)
This paper combined education data mining and spatial-temporal behavior analysis to study the prediction and analysis of college students' physical exercise behavior.(2)
This paper proposes a twofold strategy to improve the basic gray wolf algorithm.(3)
SVM is optimized with the improved gray wolf algorithm.This paper consists of five main sections: Section1 is the introduction, Section 2 is the state of the art, Section 3 is the methodology, Section 4 is the result analysis and discussion, and Section 5 is the conclusion.


## 2. State of the Art

The research content of this paper is related to educational data mining, and the core is spatiotemporal data prediction. Therefore, this section introduces relevant work from two aspects: education data mining and spatiotemporal behavior prediction.


### 2.1. Education Data Mining

At present, with the development of education informatization and exponential growth of education big data, education data mining (EDM) has become a new interdisciplinary research field. It aims to use data mining technology to explore unique data in different educational environments, better understand students and their learning environment, and improve students' learning process and education management. Due to limited data sources, most studies on educational data mining focus on the data generated by students' online learning activities, modeling and analyzing students' online learning process and learning performance. At present, the technology and field of educational data mining are relatively single and lack multi-technology and cross-field integration. At the same time, the understanding of education data is not deep, with only some simple applications.With the construction of smart campus and multisource collection of campus big data, analysis of students' daily behaviors on campus has become a research trend. Based on students' basic information data, literature [20] uses artificial neural network, support vector machine, and other basic prediction models to achieve the prediction of students' academic performance. Literature [21] analyzes students' complex behavioral data such as campus swipe card, WiFi access, and trajectory change; builds a multimode and multi-label learning model for privacy protection; and effectively predicts the subsidy level to be given to students. Literature [22] uses the data of students' campus check-in behavior to encode the correlation among students' individual, interest points and activities based on heterogeneous graph method. These researches mostly analyze and forecast the data of a specific problem or a kind of phenomenon but lack the prediction and mining of students' daily behaviors on campus.


### 2.2. Spatiotemporal Behavior Prediction

The exploration of the laws of human behavior in time and space has always been the research direction of various disciplines such as nature, economy, and society. As more and more behavioral data have been accurately recorded, scholars have been able to quantitatively analyze the temporal and spatial patterns of human behavior and its dynamics, thus improving the traditional understanding of human behavior. Although the spatiotemporal characteristics of the data are extracted, the input is limited to standard structured data, so it cannot be used to predict the behavior of graph structure.Literature [23] proposed a general graph convolution framework, which transforms the Laplace matrix eigenvector of a graph into the spectral domain to achieve approximate solution of the problem. Literature [24] first proposed the spatiotemporal graph convolution network model and solved the time series prediction problem by using the time-domain and spatiotemporal convolution structures with fewer parameters. By designing the convolution network model of spatiotemporal synchronization graph, the spatiotemporal correlation of complex data is captured effectively by using synchronous modeling mechanism. The validity of semantic trajectory extraction method for semantic enhancement of spatiotemporal behavior data is studied.


## 2.1. Education Data Mining

At present, with the development of education informatization and exponential growth of education big data, education data mining (EDM) has become a new interdisciplinary research field. It aims to use data mining technology to explore unique data in different educational environments, better understand students and their learning environment, and improve students' learning process and education management. Due to limited data sources, most studies on educational data mining focus on the data generated by students' online learning activities, modeling and analyzing students' online learning process and learning performance. At present, the technology and field of educational data mining are relatively single and lack multi-technology and cross-field integration. At the same time, the understanding of education data is not deep, with only some simple applications.With the construction of smart campus and multisource collection of campus big data, analysis of students' daily behaviors on campus has become a research trend. Based on students' basic information data, literature [20] uses artificial neural network, support vector machine, and other basic prediction models to achieve the prediction of students' academic performance. Literature [21] analyzes students' complex behavioral data such as campus swipe card, WiFi access, and trajectory change; builds a multimode and multi-label learning model for privacy protection; and effectively predicts the subsidy level to be given to students. Literature [22] uses the data of students' campus check-in behavior to encode the correlation among students' individual, interest points and activities based on heterogeneous graph method. These researches mostly analyze and forecast the data of a specific problem or a kind of phenomenon but lack the prediction and mining of students' daily behaviors on campus.


## 2.2. Spatiotemporal Behavior Prediction

The exploration of the laws of human behavior in time and space has always been the research direction of various disciplines such as nature, economy, and society. As more and more behavioral data have been accurately recorded, scholars have been able to quantitatively analyze the temporal and spatial patterns of human behavior and its dynamics, thus improving the traditional understanding of human behavior. Although the spatiotemporal characteristics of the data are extracted, the input is limited to standard structured data, so it cannot be used to predict the behavior of graph structure.Literature [23] proposed a general graph convolution framework, which transforms the Laplace matrix eigenvector of a graph into the spectral domain to achieve approximate solution of the problem. Literature [24] first proposed the spatiotemporal graph convolution network model and solved the time series prediction problem by using the time-domain and spatiotemporal convolution structures with fewer parameters. By designing the convolution network model of spatiotemporal synchronization graph, the spatiotemporal correlation of complex data is captured effectively by using synchronous modeling mechanism. The validity of semantic trajectory extraction method for semantic enhancement of spatiotemporal behavior data is studied.


## 3. Methodology

### 3.1. Improved Gray Wolf Algorithm and Simulation Experiment

Gray wolf optimization algorithm is a new meta-heuristic swarm intelligence algorithm based on gray wolf social behavior. Compared with the traditional algorithm, the basic gray wolf algorithm is easy to fall into the disadvantage of local optimization although it has a faster search speed. Therefore, this paper proposes two strategies to improve it.


#### 3.1.1. Nonlinear Decreasing Strategy of Convergence Factors

The coordination between global search performance and local search performance exists in swarm intelligence algorithms, and gray wolf algorithm is no exception. Therefore, balancing local and global search performance can improve the optimization ability of gray wolf algorithm. In the process of optimization, the cooperative coefficient vectorG value affects the exploration and development of the algorithm to a certain extent. G is affected by the value of the convergence factor g. In the standard gray wolf algorithm, a decreases linearly from 2 to 0. This linear decreasing convergence factor cannot meet the balance of global search and local search performance. Therefore, this paper proposes a nonlinear convergence factor based on sine form, as shown in the following formula:(1)g=sinπnt+π2+1,where n is the current number of iterations. t is the maximum number of iterations. The variation of the convergence factor a with the number of iterations is shown in Figure 2.Figure 2
Convergence factor comparison diagram.At the early stage of convergence, the value of the improved convergence factorg decreases relatively slowly compared to the standard algorithm. Therefore there will be a larger value of G. Expanding the search range in the early stage while ensuring the diversity of the total population of the algorithm can avoid falling into local optimum. In the late convergence stage, the improved convergence factor g decreases faster compared to the convergence factor of the standard algorithm. This also results in a smaller G value, which focuses more on local search and makes the algorithm convergence more accurate.


#### 3.1.2. Dynamic Weight Strategy Based on Euclidean Distance

In the standard gray wolf algorithm,α, β, and γ wolves have the same guidance for ω wolves. This will cause the algorithm to converge too slowly and make the algorithm easily fall into local optimum. In fact, the wolves in the gray wolf algorithm are based on social hierarchy. In order to make the wolves of the 3 leadership levels play their due contribution to accelerate the convergence of the algorithm and enhance the global optimality finding ability of the algorithm, this paper uses a dynamic weighting strategy based on Euclidean distance. This can improve the position update formula in the algorithm.According to the positionsI1, I2, I3 updated by ω wolves under the guidance of the leading gray wolves in the iterative process, the learning rates of ω wolves to α, β, and γ wolves are obtained as m1, m2, and m3, respectively, as shown in (2)–(4). Then, the position update formula becomes the form of (5) to improve the standard gray wolf algorithm.(2)m1=I1I1+I2+I3,(3)m2=I2I1+I2+I3,(4)m3=I3I1+I2+I3,(5)In+1=m1I1+m2I2+m3I33.


#### 3.1.3. Simulation Experiment

In order to verify the superior performance of the improved gray wolf algorithm, 2 single-peak functions and 2 multi-peak functions are used as test functions to conduct simulation experiments. In order to ensure the objective and reliable results, 20 independent experiments were conducted, and the test function is shown in Table1.Table 1
Benchmark functions.
FunctionThe formulaDimensionA search scopeThe minimum valueUnimodal functionSpheref1i=∑x=1tix235(−150, 150)0Schwefelf2i=∑x=1tix+∏x=1tix35(−15, 15)0Multimodal functionRastriginf3i=∑​x=1tix2−cos2πix+1035(−5.14, 5.14)0Ackleyf4i=−20exp−0.2θ∑x=1tix2−exp1/t∑x=1tcos2πix+20+e35(−35, 35)0For the above test function, set the number of wolves to 35 and the maximum iteration to 600. The CPU of the simulation experiment computer is Intel core i5–5400U, with main frequency of 4 GHz, 16 GB RAM, and Microsoft Windows 12 64-bit operating system. The computing environment is Matlab 2018(a). Through the simulation experiment, the optimization results of the four test functions are shown in Table2. As can be seen from Table 2, IGWO algorithm has obtained good global optimal solutions in all the four test functions. For Sphere and Rastrigin functions, IGWO algorithm converges to the theoretical optimal value 0. In the optimization process of Schwefel and Ackley functions, compared with the basic GWO algorithm, IGWO algorithm has more accurate optimization results. By comparing the standard deviations of the optimal values of the four functions, it can be found that the IGWO algorithm is superior to the basic GWO algorithm. In summary, the IGWO algorithm in this paper is superior to the basic GWO algorithm in solving accuracy and stability.Table 2
Test result functions of different algorithms.
FunctionGWO algorithmIGWO algorithmThe averageThe standard deviationThe averageThe standard deviationSphere1.59 × 10−272.65 × 10−2700Schwefel8.81 × 10−179.41 × 10−171.27 × 10−208Rastrigin2.142.6100Ackley1.07 × 10−131.53 × 10−144.46 × 10−152.36 × 10−30


### 3.2. Optimizing the Behavior Prediction of SVM

#### 3.2.1. Support Vector Machines

Support vector machine (SVM) is a machine learning algorithm based on statistical theory. Based on VC dimension theory, the introduction of structural risk minimization has obvious advantages in solving small sample problems. SVM mainly maps input vector to high-dimensional space through preselected nonlinear mapping and constructs regression estimation function in high-dimensional space, as shown in the following formula:(6)fi=mN⋅Φi+h,where h is the threshold value, m is a high-dimensional feature space, and Φi is a nonlinear mapping.Due to the introduction of the structural risk minimization principle, it can be translated into the following formula:(7)min12m2+12E∑x=1tξx2,s,n,jx=ΦNi+h+ξx,where E is the penalty parameter and ξ is the slack variable.To solve the optimization problem, the Lagrange operatorg is introduced and the Lagrange function is constructed. The partial derivative of the Lagrange function at the saddle point is 0. By transforming the quadratic planning problem into a new pairwise problem and introducing the kernel function, the prediction model can be obtained as follows:(8)J=∑x=1tgxZi,ix+h.


#### 3.2.2. IGWO-SVM Prediction Model

The operation steps of IGWO-SVM model for college students' physical exercise behavior prediction are as follows. The flowchart is shown in Figure3.(1)
Determine the training set and test set, and normalize the processing.(2)
Determine the gray wolf algorithm parameters (including the number of wolves and the maximum number of iterations) and the SVM hyperparameter range (upper and lower limits of penalty parameterC and kernel parameter A).(3)
Determine the initialα, β, γ wolf position and iteration. MSE was selected as fitness function, and the training set was input into SVM network to calculate the updated position of ω wolves under the guidance of gray wolves. The learning rate of ω wolves to α, β, and γ wolves was updated by (2)–(4). Track and kill the prey according to (5), use the improved gray wolf algorithm to determine the C and A values, and obtain the fitness values of C and A that meet the requirements.(4)
If the number of iterations or accuracy requirements are met, the prediction model can be obtained. Otherwise, repeat the process.(5)
Bring the test set into the trained SVM network and output the prediction results.(6)
Compare the predicted result with the expected value. Relative error and absolute error were used to evaluate the accuracy of IGWO-SVM slope stability prediction model.Figure 3
Flowchart of IGWO-SVM prediction model.


## 3.1. Improved Gray Wolf Algorithm and Simulation Experiment

Gray wolf optimization algorithm is a new meta-heuristic swarm intelligence algorithm based on gray wolf social behavior. Compared with the traditional algorithm, the basic gray wolf algorithm is easy to fall into the disadvantage of local optimization although it has a faster search speed. Therefore, this paper proposes two strategies to improve it.


### 3.1.1. Nonlinear Decreasing Strategy of Convergence Factors

The coordination between global search performance and local search performance exists in swarm intelligence algorithms, and gray wolf algorithm is no exception. Therefore, balancing local and global search performance can improve the optimization ability of gray wolf algorithm. In the process of optimization, the cooperative coefficient vectorG value affects the exploration and development of the algorithm to a certain extent. G is affected by the value of the convergence factor g. In the standard gray wolf algorithm, a decreases linearly from 2 to 0. This linear decreasing convergence factor cannot meet the balance of global search and local search performance. Therefore, this paper proposes a nonlinear convergence factor based on sine form, as shown in the following formula:(1)g=sinπnt+π2+1,where n is the current number of iterations. t is the maximum number of iterations. The variation of the convergence factor a with the number of iterations is shown in Figure 2.Figure 2
Convergence factor comparison diagram.At the early stage of convergence, the value of the improved convergence factorg decreases relatively slowly compared to the standard algorithm. Therefore there will be a larger value of G. Expanding the search range in the early stage while ensuring the diversity of the total population of the algorithm can avoid falling into local optimum. In the late convergence stage, the improved convergence factor g decreases faster compared to the convergence factor of the standard algorithm. This also results in a smaller G value, which focuses more on local search and makes the algorithm convergence more accurate.


### 3.1.2. Dynamic Weight Strategy Based on Euclidean Distance

In the standard gray wolf algorithm,α, β, and γ wolves have the same guidance for ω wolves. This will cause the algorithm to converge too slowly and make the algorithm easily fall into local optimum. In fact, the wolves in the gray wolf algorithm are based on social hierarchy. In order to make the wolves of the 3 leadership levels play their due contribution to accelerate the convergence of the algorithm and enhance the global optimality finding ability of the algorithm, this paper uses a dynamic weighting strategy based on Euclidean distance. This can improve the position update formula in the algorithm.According to the positionsI1, I2, I3 updated by ω wolves under the guidance of the leading gray wolves in the iterative process, the learning rates of ω wolves to α, β, and γ wolves are obtained as m1, m2, and m3, respectively, as shown in (2)–(4). Then, the position update formula becomes the form of (5) to improve the standard gray wolf algorithm.(2)m1=I1I1+I2+I3,(3)m2=I2I1+I2+I3,(4)m3=I3I1+I2+I3,(5)In+1=m1I1+m2I2+m3I33.


### 3.1.3. Simulation Experiment

In order to verify the superior performance of the improved gray wolf algorithm, 2 single-peak functions and 2 multi-peak functions are used as test functions to conduct simulation experiments. In order to ensure the objective and reliable results, 20 independent experiments were conducted, and the test function is shown in Table1.Table 1
Benchmark functions.
FunctionThe formulaDimensionA search scopeThe minimum valueUnimodal functionSpheref1i=∑x=1tix235(−150, 150)0Schwefelf2i=∑x=1tix+∏x=1tix35(−15, 15)0Multimodal functionRastriginf3i=∑​x=1tix2−cos2πix+1035(−5.14, 5.14)0Ackleyf4i=−20exp−0.2θ∑x=1tix2−exp1/t∑x=1tcos2πix+20+e35(−35, 35)0For the above test function, set the number of wolves to 35 and the maximum iteration to 600. The CPU of the simulation experiment computer is Intel core i5–5400U, with main frequency of 4 GHz, 16 GB RAM, and Microsoft Windows 12 64-bit operating system. The computing environment is Matlab 2018(a). Through the simulation experiment, the optimization results of the four test functions are shown in Table2. As can be seen from Table 2, IGWO algorithm has obtained good global optimal solutions in all the four test functions. For Sphere and Rastrigin functions, IGWO algorithm converges to the theoretical optimal value 0. In the optimization process of Schwefel and Ackley functions, compared with the basic GWO algorithm, IGWO algorithm has more accurate optimization results. By comparing the standard deviations of the optimal values of the four functions, it can be found that the IGWO algorithm is superior to the basic GWO algorithm. In summary, the IGWO algorithm in this paper is superior to the basic GWO algorithm in solving accuracy and stability.Table 2
Test result functions of different algorithms.
FunctionGWO algorithmIGWO algorithmThe averageThe standard deviationThe averageThe standard deviationSphere1.59 × 10−272.65 × 10−2700Schwefel8.81 × 10−179.41 × 10−171.27 × 10−208Rastrigin2.142.6100Ackley1.07 × 10−131.53 × 10−144.46 × 10−152.36 × 10−30


## 3.1.1. Nonlinear Decreasing Strategy of Convergence Factors

The coordination between global search performance and local search performance exists in swarm intelligence algorithms, and gray wolf algorithm is no exception. Therefore, balancing local and global search performance can improve the optimization ability of gray wolf algorithm. In the process of optimization, the cooperative coefficient vectorG value affects the exploration and development of the algorithm to a certain extent. G is affected by the value of the convergence factor g. In the standard gray wolf algorithm, a decreases linearly from 2 to 0. This linear decreasing convergence factor cannot meet the balance of global search and local search performance. Therefore, this paper proposes a nonlinear convergence factor based on sine form, as shown in the following formula:(1)g=sinπnt+π2+1,where n is the current number of iterations. t is the maximum number of iterations. The variation of the convergence factor a with the number of iterations is shown in Figure 2.Figure 2
Convergence factor comparison diagram.At the early stage of convergence, the value of the improved convergence factorg decreases relatively slowly compared to the standard algorithm. Therefore there will be a larger value of G. Expanding the search range in the early stage while ensuring the diversity of the total population of the algorithm can avoid falling into local optimum. In the late convergence stage, the improved convergence factor g decreases faster compared to the convergence factor of the standard algorithm. This also results in a smaller G value, which focuses more on local search and makes the algorithm convergence more accurate.


## 3.1.2. Dynamic Weight Strategy Based on Euclidean Distance

In the standard gray wolf algorithm,α, β, and γ wolves have the same guidance for ω wolves. This will cause the algorithm to converge too slowly and make the algorithm easily fall into local optimum. In fact, the wolves in the gray wolf algorithm are based on social hierarchy. In order to make the wolves of the 3 leadership levels play their due contribution to accelerate the convergence of the algorithm and enhance the global optimality finding ability of the algorithm, this paper uses a dynamic weighting strategy based on Euclidean distance. This can improve the position update formula in the algorithm.According to the positionsI1, I2, I3 updated by ω wolves under the guidance of the leading gray wolves in the iterative process, the learning rates of ω wolves to α, β, and γ wolves are obtained as m1, m2, and m3, respectively, as shown in (2)–(4). Then, the position update formula becomes the form of (5) to improve the standard gray wolf algorithm.(2)m1=I1I1+I2+I3,(3)m2=I2I1+I2+I3,(4)m3=I3I1+I2+I3,(5)In+1=m1I1+m2I2+m3I33.


## 3.1.3. Simulation Experiment

In order to verify the superior performance of the improved gray wolf algorithm, 2 single-peak functions and 2 multi-peak functions are used as test functions to conduct simulation experiments. In order to ensure the objective and reliable results, 20 independent experiments were conducted, and the test function is shown in Table1.Table 1
Benchmark functions.
FunctionThe formulaDimensionA search scopeThe minimum valueUnimodal functionSpheref1i=∑x=1tix235(−150, 150)0Schwefelf2i=∑x=1tix+∏x=1tix35(−15, 15)0Multimodal functionRastriginf3i=∑​x=1tix2−cos2πix+1035(−5.14, 5.14)0Ackleyf4i=−20exp−0.2θ∑x=1tix2−exp1/t∑x=1tcos2πix+20+e35(−35, 35)0For the above test function, set the number of wolves to 35 and the maximum iteration to 600. The CPU of the simulation experiment computer is Intel core i5–5400U, with main frequency of 4 GHz, 16 GB RAM, and Microsoft Windows 12 64-bit operating system. The computing environment is Matlab 2018(a). Through the simulation experiment, the optimization results of the four test functions are shown in Table2. As can be seen from Table 2, IGWO algorithm has obtained good global optimal solutions in all the four test functions. For Sphere and Rastrigin functions, IGWO algorithm converges to the theoretical optimal value 0. In the optimization process of Schwefel and Ackley functions, compared with the basic GWO algorithm, IGWO algorithm has more accurate optimization results. By comparing the standard deviations of the optimal values of the four functions, it can be found that the IGWO algorithm is superior to the basic GWO algorithm. In summary, the IGWO algorithm in this paper is superior to the basic GWO algorithm in solving accuracy and stability.Table 2
Test result functions of different algorithms.
FunctionGWO algorithmIGWO algorithmThe averageThe standard deviationThe averageThe standard deviationSphere1.59 × 10−272.65 × 10−2700Schwefel8.81 × 10−179.41 × 10−171.27 × 10−208Rastrigin2.142.6100Ackley1.07 × 10−131.53 × 10−144.46 × 10−152.36 × 10−30


## 3.2. Optimizing the Behavior Prediction of SVM

### 3.2.1. Support Vector Machines

Support vector machine (SVM) is a machine learning algorithm based on statistical theory. Based on VC dimension theory, the introduction of structural risk minimization has obvious advantages in solving small sample problems. SVM mainly maps input vector to high-dimensional space through preselected nonlinear mapping and constructs regression estimation function in high-dimensional space, as shown in the following formula:(6)fi=mN⋅Φi+h,where h is the threshold value, m is a high-dimensional feature space, and Φi is a nonlinear mapping.Due to the introduction of the structural risk minimization principle, it can be translated into the following formula:(7)min12m2+12E∑x=1tξx2,s,n,jx=ΦNi+h+ξx,where E is the penalty parameter and ξ is the slack variable.To solve the optimization problem, the Lagrange operatorg is introduced and the Lagrange function is constructed. The partial derivative of the Lagrange function at the saddle point is 0. By transforming the quadratic planning problem into a new pairwise problem and introducing the kernel function, the prediction model can be obtained as follows:(8)J=∑x=1tgxZi,ix+h.


### 3.2.2. IGWO-SVM Prediction Model

The operation steps of IGWO-SVM model for college students' physical exercise behavior prediction are as follows. The flowchart is shown in Figure3.(1)
Determine the training set and test set, and normalize the processing.(2)
Determine the gray wolf algorithm parameters (including the number of wolves and the maximum number of iterations) and the SVM hyperparameter range (upper and lower limits of penalty parameterC and kernel parameter A).(3)
Determine the initialα, β, γ wolf position and iteration. MSE was selected as fitness function, and the training set was input into SVM network to calculate the updated position of ω wolves under the guidance of gray wolves. The learning rate of ω wolves to α, β, and γ wolves was updated by (2)–(4). Track and kill the prey according to (5), use the improved gray wolf algorithm to determine the C and A values, and obtain the fitness values of C and A that meet the requirements.(4)
If the number of iterations or accuracy requirements are met, the prediction model can be obtained. Otherwise, repeat the process.(5)
Bring the test set into the trained SVM network and output the prediction results.(6)
Compare the predicted result with the expected value. Relative error and absolute error were used to evaluate the accuracy of IGWO-SVM slope stability prediction model.Figure 3
Flowchart of IGWO-SVM prediction model.


## 3.2.1. Support Vector Machines

Support vector machine (SVM) is a machine learning algorithm based on statistical theory. Based on VC dimension theory, the introduction of structural risk minimization has obvious advantages in solving small sample problems. SVM mainly maps input vector to high-dimensional space through preselected nonlinear mapping and constructs regression estimation function in high-dimensional space, as shown in the following formula:(6)fi=mN⋅Φi+h,where h is the threshold value, m is a high-dimensional feature space, and Φi is a nonlinear mapping.Due to the introduction of the structural risk minimization principle, it can be translated into the following formula:(7)min12m2+12E∑x=1tξx2,s,n,jx=ΦNi+h+ξx,where E is the penalty parameter and ξ is the slack variable.To solve the optimization problem, the Lagrange operatorg is introduced and the Lagrange function is constructed. The partial derivative of the Lagrange function at the saddle point is 0. By transforming the quadratic planning problem into a new pairwise problem and introducing the kernel function, the prediction model can be obtained as follows:(8)J=∑x=1tgxZi,ix+h.


## 3.2.2. IGWO-SVM Prediction Model

The operation steps of IGWO-SVM model for college students' physical exercise behavior prediction are as follows. The flowchart is shown in Figure3.(1)
Determine the training set and test set, and normalize the processing.(2)
Determine the gray wolf algorithm parameters (including the number of wolves and the maximum number of iterations) and the SVM hyperparameter range (upper and lower limits of penalty parameterC and kernel parameter A).(3)
Determine the initialα, β, γ wolf position and iteration. MSE was selected as fitness function, and the training set was input into SVM network to calculate the updated position of ω wolves under the guidance of gray wolves. The learning rate of ω wolves to α, β, and γ wolves was updated by (2)–(4). Track and kill the prey according to (5), use the improved gray wolf algorithm to determine the C and A values, and obtain the fitness values of C and A that meet the requirements.(4)
If the number of iterations or accuracy requirements are met, the prediction model can be obtained. Otherwise, repeat the process.(5)
Bring the test set into the trained SVM network and output the prediction results.(6)
Compare the predicted result with the expected value. Relative error and absolute error were used to evaluate the accuracy of IGWO-SVM slope stability prediction model.Figure 3
Flowchart of IGWO-SVM prediction model.


## 4. Result Analysis and Discussion

### 4.1. Behavioral Data Set Construction and Data Preprocessing

In order to verify the effectiveness of the proposed method in the analysis of physical activity behavior on campus, an experiment was conducted using real data. The data used in this paper were obtained from the data of students in a central university in China. All the student numbers in this data are generated by the privacy-protected student number codes. In addition, considering the uniqueness of some individuals' spatial and temporal patterns of behavior, the identifiability of the data was further reduced in the data construction stage. For example, the specific information was anonymized, the specific room number was blurred, and the location was regionalized. After the processing of raw data, it is difficult to reidentify individual students accurately. Examples of specific student campus daily activity data are shown in Table3.Table 3
Active data instances and behavior labels in the database.
Student idDateTimeAreaDescriptionBehaviorAbc1202021/9/66 : 30PlaygroundPhysical exercisePhysical activity behaviorAbc1212021/9/68 : 07Dining areaRice congeeDiet behaviorAbc1222021/9/68 : 11Dormitory areaRestRest behaviorAbc1232021/9/69 : 52The library districtI like the bookBorrowing behaviorAbc1242021/9/610 : 15Teaching and research areaFundamentals of educationA class actAbc1252021/9/611 : 39Teaching and research areaFundamentals of educationA class actAbc1262021/9/612 : 30Dining areaDumplingsDiet behaviorAbc1272021/9/613 : 00Dormitory areaRestRest behaviorAbc1282021/9/614 : 30Teaching and research areaHigher mathematicsA class actAbc1292021/9/618 : 40Basketball courtPlay basketballPhysical activity behaviorAccording to this paper, the formulaic definition of physical exercise behavior is shown in (9) and (10). Firstly, the physical exercise data are clustered to construct the data set of campus physical exercise behavior, which lays a foundation for semantic information fusion on campus. Field information such as activity time, region, location, and description in the database is used to obtain the campus behavior data set with accurate behavior meaning, as shown in the last column in Table 3. This provides the basis for data preprocessing and network input. This article is created from active field information. While constructing the data set, the activity records with clear meaning are screened, and the activity data without practical meaning is deleted.(9)gx=px,nx,lx,dx,(10)G=g1,g2,…,gn,where px∈P represents the performer of physical exercise, nx represents the time of physical exercise, lx∈L represents the place of physical exercise, dx∈D represents the specific description of physical exercise, and G represents the collection of all physical exercise in the data.In the preprocessing of data set, this paper is divided into three steps.(1)
Digitize the data labels. The specific step is to digitize the category marking features in the original data. For example, the regional category labels in the data of students' campus physical exercise behavior are numerically analyzed. This method keeps the basic characteristics of data and does not change the original data information. However, the singularities in the data will greatly affect the experimental accuracy, so the data should be further processed.(2)
Standardize the data. In data analysis, it is generally required that the original data meet normal distribution. In order to ensure the accuracy of data analysis and reduce the computational complexity, the consistency of the influence degree of different range features on the results should be ensured. The formula for data standardization is as follows:(11)k=i−μσσ,whereμ and σ are the mean and standard deviation of the features in the original data set, respectively. For example, the exercise duration, exercise items, and other numerical characteristics of students' campus physical exercise behavior data can be standardized to make the data standardized and more conducive to data analysis.(3)
Normalize data: after the numerical value of the category tag features in the original data, the linear function is normalized, as shown in the following formula:(12)Inorm=I−IminImax−Imin,I is the original data. Imax and Iminare the maximum and minimum values in the original data, respectively. Inorm is the characteristic result after normalization. In this way, equal scale of data can be achieved to avoid the influence of outliers on data analysis.During the experiment, the data of 396 students from a college from September 1, 2020, to January 20, 2021, totaled about 140 days, 20 weeks, and 5 months, and a total of 7605741 activity records were used. In addition, when constructing the semantic network of college students' physical exercise, in order to better connect the association between time-series data, according to the behavioral characteristics of physical exercise data set, the semantic network was constructed as a graph data structure containing time points and spatial positions, that is, 9 campus exercise areas and 30 time points. The dimension of adjacency matrix of graph structure is 168 × 168. Based on this, the convolution operation of physical exercise behavior characteristic map of three time segments is realized.


### 4.2. Experimental Methods and Analysis

Since there are few studies on the prediction of students' campus physical exercise behavior, this paper adopts several typical machine learning prediction models such as literature [25], literature [26], literature [27], and literature [28] to conduct comparative analysis of experimental results with the method in this paper.The network structure of the model in this paper includes three time segments, which are daily segment, weekly segment, and monthly segment. Each segment has one level of standard two-dimensional convolution of time dimension and two levels of spatial convolution. Experiments were carried out on data sets; 70% of the physical exercise behavior data were set as training set, and 30% of the physical exercise behavior data were set as verification set. The main evaluation indexes include accuracy (Acc), precision (Pre), recall rate (Rec), andF1. The calculation formulas are as follows:(13)Acc=TP+TNTP+FP+FN+TN,Pre=TPTP+FP,Rec=TPTP+FN,F1=2×Pre×RecPre+Rec.TP represents the number of samples whose positive class behavior is predicted by the model. FP represents the number of samples whose negative class behavior is predicted to be positive by the model. FN represents the number of samples whose positive class behavior is predicted to be negative class by the model. TN represents the number of samples whose negative class behavior is predicted to be negative.TheAcc and F1 values predicted by the different model behaviors in the experiments of this paper are shown in Table 4. The accuracy and F1 of the models in this paper are higher than those in the literature [25–28]. This proves that the model in this paper is more effective in analyzing the data after considering the temporal period correlation and spatial semantic correlation together. Compared with four typical machine learning prediction models in the literature [25–28], this model has obvious advantage, which proves that using this model to solve the problem of campus road network data is more consistent with the basic structure of the data. Compared with the literature [25–28], this paper's model has the highest prediction accuracy for college students' physical exercise behavior. This is because the model in this paper considers the campus semantic features at the spatial level. Meanwhile, the periodic features of daily, weekly, and monthly segments of college students' physical exercise behaviors are considered at the temporal level to achieve more accurate feature extraction in the prediction process.Table 4
Experimental results of different algorithms in data sets.
ModelAcc (%)F1 (%)Literature [25]84.3482.37Literature [26]81.0880.17Literature [27]78.6275.35Literature [28]86.4585.17Proposed90.4590.27Figure4 shows the Acc of different models for different categories of physical exercise behavior prediction. The model in this paper outperformed other models in different behavior categories. The highest prediction accuracy for physical exercise behavior proves that it can effectively obtain the periodicity of students' campus physical exercise behavior data and improve the accuracy of behavior prediction.Figure 4
Comparison of prediction accuracy of different types of behavior.Table5 compares the number of parameters of the above four methods and the training time for obtaining the optimal behavior prediction effect. Since the semantic characteristic information of road network is considered in the construction of adjacency matrix, the number of parameters is larger than those of the other models, and the training process is also the most time-consuming. The attention mechanism was added in [25, 26] to increase the number of references. It also improves the accuracy of predictions. Literature [28] synchronizes time and space convolution to improve algorithm efficiency. However, the model in this paper is more suitable for the prediction of students' physical exercise behavior that can construct semantic network. It can extract more valuable semantic information of physical exercise, and its accuracy is better than that of the other methods. Longer training duration is also acceptable for the mining of massive physical exercise daily behavior data.Table 5
Comparison of training time and model parameters.
ModelTraining time (s)Number of parametersLiterature [25]956.847451Literature [26]1030.657583Literature [27]979.167918Literature [28]757.657649Proposed1075.6910263


### 4.3. Individual Behavior and Habit Information Mining

Taking a specific student as the research object, we screened a total of 6845 campus activity data of the student from the data set. The model with better training effect was used to predict the student's behavior on the day, week, and month, and the behavior rule was found by data correlation analysis. Table6 shows the statistical analysis of behaviors in some time periods of a day. It can be found that the behavior of the student at 7 : 00 is mainly resting behavior; the behavior between 8 : 00 and 12 : 00 is mainly class behavior and eating behavior; and the behavior between 14 : 00 and 18 : 00 is mainly class behavior, self-study behavior, and eating behavior. In the statistical analysis of behavior rules in different time periods, there are few deviating behaviors from the mean, and the whole has no influence on the statistics of the mean. From a macroscopic point of view, it is proved that the student has good daily behavior habits. Habits refer to regular behavior patterns, which are formed by the generalization of behavior sequences with repeated cycles and overlapping time and space. This paper establishes strong correlation analysis between individual habit discovery and campus behavior data. This paper deduces the nature of behavior by data-driven method and obtains the correlation between personalized behavior habits and academic growth. Therefore, suggestions and references based on daily behavior level are provided for personalized training of college students.Table 6
Analysis of students’ daily behavioral habits.
CategoryTime789101112131415161718Other acts000000000000Exercise behavior000000000000Reading behavior000000000000Self-study behavior000000011111Classroom behavior011111011111Eating behavior011111011111Rest behavior100000100000Note. 0 means the behavior did not happen. 1 means the behavior happened.


## 4.1. Behavioral Data Set Construction and Data Preprocessing

In order to verify the effectiveness of the proposed method in the analysis of physical activity behavior on campus, an experiment was conducted using real data. The data used in this paper were obtained from the data of students in a central university in China. All the student numbers in this data are generated by the privacy-protected student number codes. In addition, considering the uniqueness of some individuals' spatial and temporal patterns of behavior, the identifiability of the data was further reduced in the data construction stage. For example, the specific information was anonymized, the specific room number was blurred, and the location was regionalized. After the processing of raw data, it is difficult to reidentify individual students accurately. Examples of specific student campus daily activity data are shown in Table3.Table 3
Active data instances and behavior labels in the database.
Student idDateTimeAreaDescriptionBehaviorAbc1202021/9/66 : 30PlaygroundPhysical exercisePhysical activity behaviorAbc1212021/9/68 : 07Dining areaRice congeeDiet behaviorAbc1222021/9/68 : 11Dormitory areaRestRest behaviorAbc1232021/9/69 : 52The library districtI like the bookBorrowing behaviorAbc1242021/9/610 : 15Teaching and research areaFundamentals of educationA class actAbc1252021/9/611 : 39Teaching and research areaFundamentals of educationA class actAbc1262021/9/612 : 30Dining areaDumplingsDiet behaviorAbc1272021/9/613 : 00Dormitory areaRestRest behaviorAbc1282021/9/614 : 30Teaching and research areaHigher mathematicsA class actAbc1292021/9/618 : 40Basketball courtPlay basketballPhysical activity behaviorAccording to this paper, the formulaic definition of physical exercise behavior is shown in (9) and (10). Firstly, the physical exercise data are clustered to construct the data set of campus physical exercise behavior, which lays a foundation for semantic information fusion on campus. Field information such as activity time, region, location, and description in the database is used to obtain the campus behavior data set with accurate behavior meaning, as shown in the last column in Table 3. This provides the basis for data preprocessing and network input. This article is created from active field information. While constructing the data set, the activity records with clear meaning are screened, and the activity data without practical meaning is deleted.(9)gx=px,nx,lx,dx,(10)G=g1,g2,…,gn,where px∈P represents the performer of physical exercise, nx represents the time of physical exercise, lx∈L represents the place of physical exercise, dx∈D represents the specific description of physical exercise, and G represents the collection of all physical exercise in the data.In the preprocessing of data set, this paper is divided into three steps.(1)
Digitize the data labels. The specific step is to digitize the category marking features in the original data. For example, the regional category labels in the data of students' campus physical exercise behavior are numerically analyzed. This method keeps the basic characteristics of data and does not change the original data information. However, the singularities in the data will greatly affect the experimental accuracy, so the data should be further processed.(2)
Standardize the data. In data analysis, it is generally required that the original data meet normal distribution. In order to ensure the accuracy of data analysis and reduce the computational complexity, the consistency of the influence degree of different range features on the results should be ensured. The formula for data standardization is as follows:(11)k=i−μσσ,whereμ and σ are the mean and standard deviation of the features in the original data set, respectively. For example, the exercise duration, exercise items, and other numerical characteristics of students' campus physical exercise behavior data can be standardized to make the data standardized and more conducive to data analysis.(3)
Normalize data: after the numerical value of the category tag features in the original data, the linear function is normalized, as shown in the following formula:(12)Inorm=I−IminImax−Imin,I is the original data. Imax and Iminare the maximum and minimum values in the original data, respectively. Inorm is the characteristic result after normalization. In this way, equal scale of data can be achieved to avoid the influence of outliers on data analysis.During the experiment, the data of 396 students from a college from September 1, 2020, to January 20, 2021, totaled about 140 days, 20 weeks, and 5 months, and a total of 7605741 activity records were used. In addition, when constructing the semantic network of college students' physical exercise, in order to better connect the association between time-series data, according to the behavioral characteristics of physical exercise data set, the semantic network was constructed as a graph data structure containing time points and spatial positions, that is, 9 campus exercise areas and 30 time points. The dimension of adjacency matrix of graph structure is 168 × 168. Based on this, the convolution operation of physical exercise behavior characteristic map of three time segments is realized.


## 4.2. Experimental Methods and Analysis

Since there are few studies on the prediction of students' campus physical exercise behavior, this paper adopts several typical machine learning prediction models such as literature [25], literature [26], literature [27], and literature [28] to conduct comparative analysis of experimental results with the method in this paper.The network structure of the model in this paper includes three time segments, which are daily segment, weekly segment, and monthly segment. Each segment has one level of standard two-dimensional convolution of time dimension and two levels of spatial convolution. Experiments were carried out on data sets; 70% of the physical exercise behavior data were set as training set, and 30% of the physical exercise behavior data were set as verification set. The main evaluation indexes include accuracy (Acc), precision (Pre), recall rate (Rec), andF1. The calculation formulas are as follows:(13)Acc=TP+TNTP+FP+FN+TN,Pre=TPTP+FP,Rec=TPTP+FN,F1=2×Pre×RecPre+Rec.TP represents the number of samples whose positive class behavior is predicted by the model. FP represents the number of samples whose negative class behavior is predicted to be positive by the model. FN represents the number of samples whose positive class behavior is predicted to be negative class by the model. TN represents the number of samples whose negative class behavior is predicted to be negative.TheAcc and F1 values predicted by the different model behaviors in the experiments of this paper are shown in Table 4. The accuracy and F1 of the models in this paper are higher than those in the literature [25–28]. This proves that the model in this paper is more effective in analyzing the data after considering the temporal period correlation and spatial semantic correlation together. Compared with four typical machine learning prediction models in the literature [25–28], this model has obvious advantage, which proves that using this model to solve the problem of campus road network data is more consistent with the basic structure of the data. Compared with the literature [25–28], this paper's model has the highest prediction accuracy for college students' physical exercise behavior. This is because the model in this paper considers the campus semantic features at the spatial level. Meanwhile, the periodic features of daily, weekly, and monthly segments of college students' physical exercise behaviors are considered at the temporal level to achieve more accurate feature extraction in the prediction process.Table 4
Experimental results of different algorithms in data sets.
ModelAcc (%)F1 (%)Literature [25]84.3482.37Literature [26]81.0880.17Literature [27]78.6275.35Literature [28]86.4585.17Proposed90.4590.27Figure4 shows the Acc of different models for different categories of physical exercise behavior prediction. The model in this paper outperformed other models in different behavior categories. The highest prediction accuracy for physical exercise behavior proves that it can effectively obtain the periodicity of students' campus physical exercise behavior data and improve the accuracy of behavior prediction.Figure 4
Comparison of prediction accuracy of different types of behavior.Table5 compares the number of parameters of the above four methods and the training time for obtaining the optimal behavior prediction effect. Since the semantic characteristic information of road network is considered in the construction of adjacency matrix, the number of parameters is larger than those of the other models, and the training process is also the most time-consuming. The attention mechanism was added in [25, 26] to increase the number of references. It also improves the accuracy of predictions. Literature [28] synchronizes time and space convolution to improve algorithm efficiency. However, the model in this paper is more suitable for the prediction of students' physical exercise behavior that can construct semantic network. It can extract more valuable semantic information of physical exercise, and its accuracy is better than that of the other methods. Longer training duration is also acceptable for the mining of massive physical exercise daily behavior data.Table 5
Comparison of training time and model parameters.
ModelTraining time (s)Number of parametersLiterature [25]956.847451Literature [26]1030.657583Literature [27]979.167918Literature [28]757.657649Proposed1075.6910263


## 4.3. Individual Behavior and Habit Information Mining

Taking a specific student as the research object, we screened a total of 6845 campus activity data of the student from the data set. The model with better training effect was used to predict the student's behavior on the day, week, and month, and the behavior rule was found by data correlation analysis. Table6 shows the statistical analysis of behaviors in some time periods of a day. It can be found that the behavior of the student at 7 : 00 is mainly resting behavior; the behavior between 8 : 00 and 12 : 00 is mainly class behavior and eating behavior; and the behavior between 14 : 00 and 18 : 00 is mainly class behavior, self-study behavior, and eating behavior. In the statistical analysis of behavior rules in different time periods, there are few deviating behaviors from the mean, and the whole has no influence on the statistics of the mean. From a macroscopic point of view, it is proved that the student has good daily behavior habits. Habits refer to regular behavior patterns, which are formed by the generalization of behavior sequences with repeated cycles and overlapping time and space. This paper establishes strong correlation analysis between individual habit discovery and campus behavior data. This paper deduces the nature of behavior by data-driven method and obtains the correlation between personalized behavior habits and academic growth. Therefore, suggestions and references based on daily behavior level are provided for personalized training of college students.Table 6
Analysis of students’ daily behavioral habits.
CategoryTime789101112131415161718Other acts000000000000Exercise behavior000000000000Reading behavior000000000000Self-study behavior000000011111Classroom behavior011111011111Eating behavior011111011111Rest behavior100000100000Note. 0 means the behavior did not happen. 1 means the behavior happened.


## 5. Conclusion

Aiming at overcoming the problems of insufficient data mining of college students' physical exercise behavior and insufficient consideration of temporal and spatial correlation in existing research methods, this paper proposes an improved gray wolf algorithm and support vector machine algorithm to predict and analyze college students' physical exercise behavior. The algorithm adopts two improved strategies to optimize the gray wolf algorithm to accelerate the convergence of the gray wolf algorithm and obtain a higher precision solution. By combining IGWO algorithm with support vector machine (SVM), the improved gray wolf optimization algorithm has strong global search ability and high search efficiency to determine the parameters of SVM, so that the nonlinear mapping ability of SVM is strengthened. Experiments show that the accuracy andF1 of the proposed algorithm are higher than those of other comparison algorithms. The algorithm in this paper can not only predict college students' physical exercise behavior with good accuracy, but also deduce the correlation between college students' personalized behavior habits and their academic growth according to the results of behavior prediction. Therefore, it provides suggestions and references based on daily behavior level for personalized training of college students, in order to correct abnormal behaviors of students, so that they can better complete their studies. The next step is to increase the richness and granularity of the data set. At the same time, more data preprocessing methods are tried to make the prediction more accurate.



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*Source: 4943510-2022-08-31.xml*