# Evaluation Method of the Influence of Sports Training on Physical Index Based on Deep Learning

Authors: Zhongxiao Wang
Journal: Security and Communication Networks
 (2021)

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## Abstract

With the rapid development of deep learning, computer vision has also become a rapidly developing field in the field of artificial intelligence. Combining the physical training of deep learning will bring good practical value. Physical training has different effects on people’s body shape, physical function, and physical quality. It is mainly reflected in the changes of relevant physical indicators after physical training. Therefore, the purpose of this article is to study the method of evaluating the impact of sports training on physical indicators based on deep learning. This paper mainly uses the convolutional neural network in deep learning to design sports training, then constructs the evaluation system of physical index impact, and finally uses the deep learning algorithm to evaluate the impact of physical index. The experimental results show that the accuracy of the algorithm proposed in this paper is significantly higher than that of the other three algorithms. Firstly, in the angular motion, the accuracy of the mean algorithm is 0.4, the accuracy of the variance algorithm is 0.2, the accuracy of the RFE algorithm is 0.4, and the accuracy of the DLA algorithm is 0.6. Similarly, in foot racing and skill sports, the accuracy of the algorithm proposed in this paper is significantly higher than that of other algorithms. Therefore, the method proposed in this paper is more effective in the evaluation of the impact of physical training on physical indicators.


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## Body

## 1. Introduction

In recent years, with the emergence of big data and the substantial increase in computer parallel computing capabilities, deep learning has become a research hotspot relying on rich training data and powerful feature expression capabilities [1, 2]. Recently, deep convolutional neural networks have been shown to have great potential in video recognition tasks, such as video interpretation, action recognition, spatiotemporal action detection, and spatiotemporal action localization [3, 4]. A deep convolutional neural network suitable for video sequences can be used to extract key event frames in motion videos to help users review and analyze actions [5, 6]. Big data include structured, semistructured, and unstructured data. Unstructured data have increasingly become the main part of data. 80% of the data in the enterprise is unstructured data, which grows exponentially by 60% every year. Against the backdrop of technological innovation represented by cloud computing, these data that originally seemed difficult to collect and use began to be easily used. Through continuous innovation in all walks of life, big data will gradually create more value for mankind. Theory is not only a necessary way of cognition but also a baseline widely recognized and disseminated. Here, understand the overall description and characterization of big data by the industry from the definition of big data characteristics, deeply analyze the value of big data from the discussion of the value of big data, insight into the development trend of big data, and examine the long-term game between people and data from the special and important perspective of big data privacy.Many scholars have studied the evaluation method of the impact of physical training on physical indicators. The research on the impact evaluation method of physical indicators abroad began earlier, and the research content and results are relatively rich [7]. As early as the 1860s, Ge et al., American medical doctors, took students as research objects to test their physical ability [8]. Zhu et al. focused on analyzing the characteristics of body mass index (BMI) of adults in Jiangsu Province by using the national physique monitoring data of Jiangsu Province in 2000 and the method of mathematical statistical analysis [9]. Oktaviani et al. selected the physique monitoring data in 2015, based on the national physique monitoring bulletin in 2014, and used statistical methods to group and compare the male faculty members of Zhejiang Normal University [10]. Noman et al. used the national survey data of students’ physique and health from 1985 to 2014 to analyze the change trend of excellent rate of students’ physique and health in different years and the difference of excellent rate of students with different characteristics and used the log-binomial regression model to analyze the relevant factors of students’ physique and health [11]. Although the academic research results on the evaluation methods of the impact of physical training on physical indicators are relatively rich, the evaluation methods of the impact of physical training on physical indicators based on in-depth learning are relatively few. Therefore, the evaluation method of the impact of physical training on physical indicators based on deep learning is of great significance. Deep learning is a kind of machine learning, and machine learning is the only way to realize artificial intelligence. The concept of deep learning comes from the research of artificial neural network. Multilayer perceptron with multiple hidden layers is a deep learning structure. Deep learning combines low-level features to form more abstract high-level representation attribute categories or features, so as to find the distributed feature representation of data. The motivation of studying deep learning is to establish a neural network that simulates the human brain for analytical learning. It simulates the mechanism of the human brain to interpret data, such as images, sounds, and texts.Firstly, this paper introduces deep convolutional neural network, which mainly includes two aspects, namely, convolutional neural network and the design of convolutional network structure, and then constructs the physical index impact evaluation system and sports training database. Finally, experiments are carried out to compare and analyze the accuracy of the algorithm. The characteristics of deep learning include emphasizing the depth of model structure. The importance of feature learning is clarified. In other words, the feature representation of the sample in the original space is transformed into a new feature space through layer by layer feature transformation, so as to make classification or prediction easier. Compared with the method of constructing features by artificial rules, using big data to learn features can better describe the rich internal information of data.


## 2. Evaluation Method of the Influence of Sports Training on Physical Index Based on Deep Learning

### 2.1. Deep Convolutional Neural Network

#### 2.1.1. Convolutional Neural Network

Convolutional neural networks are mainly composed of five different functional layers, which are input layer, convolution layer, pooling layer, fully connected layer, and output layer. According to different functions, convolutional neural networks can be divided into feature extractors and classifiers. Feature extractors mainly include input layer, convolution layer, and pooling layer, and the rest belong to classifiers [12]. The working principle of the feature extractor is that the convolutional layer separately calculates the feature map output after the activation function of the local features of the input image through the convolution calculation, and the classifier output after the feature map is compressed. The classifier is to integrate and map the extracted statistical features into the tag space of the sample and produce the output of the task. The basic structure of the convolutional neural network is shown in Figure 1.Figure 1
Basic structure of convolutional neural network.(1) Convolutional Layer. Convolution is the mathematical operation of two functions, that is, the integral of the product of one function g flipped and evaluated with the other function f, denoted as g∗f. The definition of continuous convolution is(1)f∗gx=∫fτgx−τdτ.Discrete convolution is defined as(2)f∗gn=∑m=−∞∞fmgn−m.(2) Pooling Layer. The pooling layer mainly performs corresponding statistical calculations on the adjacent output of a certain position and uses the result to represent the output of the network in that area. Another important function of the pooling operation is to pool the inputs of different sizes into a uniform size output.(3) Activation Layer. The function of the activation layer is mainly to perform nonlinear transformation on the input data. The convolutional layer without activation function is equivalent to matrix multiplication, which is a linear transformation. The activation function can add nonlinear factors to map the features to the nonlinear interval, so as to solve the problems that cannot be solved by the linear model. Commonly used activation functions are Sigmoid function, Tanh, ReLU function, and so on.(4) Fully Connected Layer. The fully connected layer mainly extracts the features of the previous convolutional layer and the pooling layer and composes more informative features. The parameters of the fully connected layer are mainly determined by the dimensions of the input and output and are not translation invariant.Top down supervised learning is to train through labeled data, transmit the error from top to bottom, and fine tune the network. Based on the parameters of each layer obtained in the first step, the parameters of the whole multilayer model are further optimized. This step is a supervised training process. The first step is similar to the random initialization initial value process of neural network. Because the first step is not random initialization, but obtained by learning the structure of input data, this initial value is closer to the global optimization, so it can achieve better results. Therefore, the good effect of deep learning is largely due to the first step of feature learning.


#### 2.1.2. Design of Convolutional Neural Network

(1) VGGNet Network Structure. The VGGNet network model repeatedly stacks 3 × 3 small convolution kernels and 2 × 2 maximum pooling layers to improve performance by continuously deepening the network. Compared with the previous excellent network structure, the error rate of this network model is lower. At the same time, the generalization ability of the network model is good, and it can have good performance on different image data sets, so the VGG network model is often used for feature extraction.In addition, VGGNet uses 3 × 3 convolution because convolution not only involves the amount of calculation but also affects the receptive field. The former is related to whether it is convenient to deploy to the mobile terminal, whether it can meet real-time processing, whether it is easy to train, etc. the latter is related to parameter update, the size of feature map, whether there are enough features extracted, model complexity, and parameter quantity, etc. It is noted that, under the same stripe, the characteristic graphs and convolution parameters of different convolution kernel sizes have little difference. The larger the convolution kernel, the greater the amount of computation.(2) ResNet Network Structure. The ResNet network proposes a connection method called short-circuit connection, which can solve the problem of gradient disappearance, as shown in Figure 2.Figure 2
Residual error module.As shown in Figure2, RESNET network introduces the residual module. Compared with the conventional nonlinear mapping, it also adds the identity mapping connected by jumping to obtain the potential mapping f (x) + X. For the network without jump connection, due to the phenomenon of gradient disappearance or gradient explosion, when the number of network layers is too many, backpropagation cannot be propagated to the shallow network. For the network with jump connection, in extreme cases, the nonlinear mapping is mapped to zero, and the expected potential mapping becomes an identity mapping, which is equivalent to “jumping” the middle structure. Once the residual mapping learns the effective parameters, it will increase the overall generalization ability of the residual network.In practice, the equivalent mapping function may not be so well optimized, but for residual learning, the solver will find disturbances more easily according to the input equivalent mapping. In short, it is much easier than learning an equivalent mapping function directly. According to the experiment, it can be found that the response value of the learned residual function is usually small, and the equivalent mapping provides a reasonable prerequisite.(3) Inception Network Structure. Inception improves the overall performance of the neural network by increasing the width of the network. The specific structure is shown in Figure 3.Figure 3
Inception module.By introducing the Inception module, the inception network structure connects the 1 × 1, 3 × 3, and 5 × 5 convolutional layer and the 3 × 3 pooling layer in parallel and finally superimposes the feature blocks of each branch and changes the traditional parallel structure is used.This paper optimizes on the basis of the algorithm based on deep learning and adds the idea of ResNet to the inception part so that the input feature map and the new feature map output by the convolutional layer are spliced, thereby increasing the diversity of features.The carefully designed inception module of inception net improves the performance of the model and reduces the amount of parameters. Generally speaking, to improve the expression ability of convolution network, it mainly depends on increasing the number of output channels, but the side effects are the increase of calculation and overfitting. Therefore, inception net uses branch networks stacked together to produce larger channel outputs. Because this is an excellent network, it can organize information across channels, improve the expression ability of the network, provide more nonlinear transformations, and have high cost performance. At the same time, the convolution and size in the network are also different, which can increase the adaptability of the network to different scales. Therefore, increasing the width and depth of the network by branching can improve the performance of the network and avoid overfitting.


### 2.2. Construction of the Evaluation System for the Impact of Physical Indicators

#### 2.2.1. The Goal of Building the System

(1)
Understanding the development status of school physical education and transforming physical indicators into operable measurement procedures and texts are important means to understand the development status of school physical education. Moreover, through a unified and homogeneous evaluation, it is possible to analyze the current situation and effectiveness of the implementation of school physical education and to provide a direction for improving education.(2)
Understanding the results of individual students’ physical education learning. We can understand how many learning results students have obtained in the whole process of physical education learning and provide a basis for the curriculum reform and teaching.


#### 2.2.2. Principles of Constructing the System

(1)
The principle of scientificity: scientificity is the basis for achieving standardization and unification of parity indicators. The purpose of establishing a physical index impact evaluation system is to objectively reflect the level of students’ physical quality and analyze the main factors that affect the level of students’ physical quality so that teachers, parents, and students can clearly understand and improve their physical quality.(2)
Systematic principle: the system has the characteristics of purpose, relevance, hierarchy, and integrity, and the index system is a comprehensive body that contains rich information and comprehensively covers all aspects of the research object. When constructing indicators, it should be based on the needs of students, the development of knowledge, and the changes in society. It is necessary to pay attention to the convergence of the vertical system and the integration of the horizontal system.(3)
The guiding principle: the establishment of the index system is to achieve a certain purpose, which is convenient for comparison, analysis, and improvement. Operability is an important criterion for measuring the index.(4)
The principle of comprehensiveness: as a multifactor comprehensive evaluation, the indicators selected for the evaluation of students’ physical quality level should be comprehensive and try to avoid the appearance of excessively single or partial indicators, resulting in unreasonable evaluation results. When setting the initial indicators, the factors to choose should be more and more comprehensive so that it is more conducive to further optimization of the indicators. However, at the same time, attention should be paid to the establishment of indicators and the selection of data should be generalized, and the degree of repetition should not be too high.


#### 2.2.3. Constructing the Framework of the Index System

The framework of constructing the index system is actually to determine the interrelationship and hierarchical structure between the indexes in the evaluation index system. The simplest form of the indicator structure is the two-layer structure and the three-layer structure. The two-layer structure mainly includes the overall target layer and the index layer. This structure does not classify the index structure. The three-tier structure includes the target layer, the subtarget layer, and the indicator layer. This article uses a three-tier structure.


#### 2.2.4. Specific Steps to Construct System Indicators

(1) Theoretical Preparation. First of all, we must master the theories and methods of evaluation in education and sports and fully understand the current situation of students’ physical indicators and the problems and deficiencies in the evaluation standards, as well as the abilities that students should have in order to achieve the indicators, and systematically collect the elements of physical indicators evaluation. Establish a scientific and reasonable index system to make adequate preparations.(2) Primary Selection of Indicator System. Complete data collection, read relevant literature and books as required, make theoretical preparations, and have a certain foundation for research theories and methods. Then, through literature analysis, brainstorming and other methods are used to determine the single index. Finally, the analytic hierarchy process is used to construct the body index impact evaluation system.Besides, according to the nature of the problem and the overall goal to be achieved, the analytic hierarchy process decomposes the problem into different constituent factors and aggregates and combines the factors according to different levels according to the correlation, influence, and subordinate relationship between the factors to form a multilevel analysis structure model, so as to finally reduce the problem to the lowest level (scheme for decision-making measures, etc.) determination of relatively important weights relative to the highest level (overall goal) or arrangement of relative advantages and disadvantages.(3) Test of Indicator System. For the system that was established for the first time, the Delphi method was used and experts were asked to conduct two rounds of tests, and then, the indicator system was optimized to make the entire system more complete and more reasonable.(4) Index Weight Distribution of the Index System. The index weight distribution of the index system is the key. The relevant experts in physical education research are selected, and the analytic hierarchy process is used to distribute the index weights, hoping that the distribution of index weights can truly reflect the characteristics of students’ sports quality and the sports that need to be strengthened.


### 2.3. Database Establishment

Note that the data processing method approximately describes or compares the functional relationship between the coordinates represented by the discrete point group on the plane with the continuous curve. A common method is to use analytical expressions to approximate discrete data. Curve linearization is one of the important means of curve fitting. For some nonlinear data, it can be linearized by simple variable transformation so that the linear equation of the transformed variable can be obtained according to the principle of the least square method.


#### 2.3.1. Sports Training Database

In order to study the impact of physical training on physical indicators, a database of the impact of physical training on physical indicators is constructed based on real data. The data resources of the database come from the actual measurement of 400 college students majoring in Physical Education in a university in our province. Firstly, according to physical training, it is divided into wrestling, foot racing, and skills. In addition, in order to reflect the impact of different sports on physical indicators, this paper also adds nonsports. Therefore, the data used in this paper are divided into four categories: wrestling, tug of war, and wrist breaking. Foot racing mainly includes rope skipping, eagle catching chicken, football, and running. Skill sports mainly include rubber band jumping, throwing sandbags, shuttlecock kicking, and house jumping.


#### 2.3.2. Physical Indicators

There are 24 physical indicators in the sports training database, which are divided into three categories. The first is the physical shape indicators, which mainly include height, weight, chest circumference, waist circumference, hip circumference, shoulder width, sitting height, pelvic width, and body fat percentage; the tightness of the upper hip is poor. Physical function indicators mainly include vital capacity, basal heart rate, maximum oxygen uptake, heart function index, and pulse pressure difference. The last category is sports quality indicators, one includes standing long jump, push-ups, sit-ups, 50 m sprints, sitting forward bending, grip strength, cross running, back-and-forth running, and back muscle strength.


#### 2.3.3. Training Settings

Students are divided into 4 groups, each corresponding to a category. First, each group will be tested for the physical index mark before training, and then, a dedicated person will guide each group to conduct the corresponding training. They will exercise 4 times a week, each exercise for 30 minutes, and then add with a preparation time of 7 minutes and an end time of 3 minutes; the experiment time is 2 months. The body index data before training are denoted as A1, and the body index data after training are denoted as A2. Table1 shows some of the data.Table 1
Examples of database measurement data.
Person123A1A2A1A2A1A2Height (cm)158.1158.2167.8167.8187.4187.5Sitting height (cm)82.582.692.592.5110.4110.450 m sprint (s)9.49.198.88.48.2Average grip strength (kg)21.526.226.528.430.432.5Back muscle strength (kg)57.160.247.555.876.879.5


#### 2.3.4. Ranking of the Degree of Influence of Physical Indicators

After performing data cleaning and data preprocessing on the collected data, a total of 400 people’s body index change data are obtained and then classified and labeled and further obtain the sorting formula of the real influence degree of sports training on the body index, as shown in(3)ImpactNxa=∑m=1zvotem,xa.Among them,ImpactNxa represents the importance of the Nth type of sports to the ath physical index, m represents the mth voter, and z represents the total number of voters.Then, experts are asked to rank and vote on the respective influence levels of the 24 indicators that have been measured for wrestling, competition, and technical sports, to obtain an orderly sequence of the top 5 physical indicators with significant impacts of the three types of sports and construct physical indicators for sports. The actual degree of influence of the impact will eventually form a database of the physical index impact of sports training. The results are shown in Table2.Table 2
The true ranking of the impact of various sports on physical indicators.
Wrestling sportsRace foot sportsAcrobatic sports1Back muscle strengthBack and forthHeart function index2GripCross direction run50 m sprint3Poor upper hip tightness50 m sprintCross direction run4Heart function indexStanding long jumpBack and forth5Poor breathingHeart function indexSit-ups


## 2.1. Deep Convolutional Neural Network

### 2.1.1. Convolutional Neural Network

Convolutional neural networks are mainly composed of five different functional layers, which are input layer, convolution layer, pooling layer, fully connected layer, and output layer. According to different functions, convolutional neural networks can be divided into feature extractors and classifiers. Feature extractors mainly include input layer, convolution layer, and pooling layer, and the rest belong to classifiers [12]. The working principle of the feature extractor is that the convolutional layer separately calculates the feature map output after the activation function of the local features of the input image through the convolution calculation, and the classifier output after the feature map is compressed. The classifier is to integrate and map the extracted statistical features into the tag space of the sample and produce the output of the task. The basic structure of the convolutional neural network is shown in Figure 1.Figure 1
Basic structure of convolutional neural network.(1) Convolutional Layer. Convolution is the mathematical operation of two functions, that is, the integral of the product of one function g flipped and evaluated with the other function f, denoted as g∗f. The definition of continuous convolution is(1)f∗gx=∫fτgx−τdτ.Discrete convolution is defined as(2)f∗gn=∑m=−∞∞fmgn−m.(2) Pooling Layer. The pooling layer mainly performs corresponding statistical calculations on the adjacent output of a certain position and uses the result to represent the output of the network in that area. Another important function of the pooling operation is to pool the inputs of different sizes into a uniform size output.(3) Activation Layer. The function of the activation layer is mainly to perform nonlinear transformation on the input data. The convolutional layer without activation function is equivalent to matrix multiplication, which is a linear transformation. The activation function can add nonlinear factors to map the features to the nonlinear interval, so as to solve the problems that cannot be solved by the linear model. Commonly used activation functions are Sigmoid function, Tanh, ReLU function, and so on.(4) Fully Connected Layer. The fully connected layer mainly extracts the features of the previous convolutional layer and the pooling layer and composes more informative features. The parameters of the fully connected layer are mainly determined by the dimensions of the input and output and are not translation invariant.Top down supervised learning is to train through labeled data, transmit the error from top to bottom, and fine tune the network. Based on the parameters of each layer obtained in the first step, the parameters of the whole multilayer model are further optimized. This step is a supervised training process. The first step is similar to the random initialization initial value process of neural network. Because the first step is not random initialization, but obtained by learning the structure of input data, this initial value is closer to the global optimization, so it can achieve better results. Therefore, the good effect of deep learning is largely due to the first step of feature learning.


### 2.1.2. Design of Convolutional Neural Network

(1) VGGNet Network Structure. The VGGNet network model repeatedly stacks 3 × 3 small convolution kernels and 2 × 2 maximum pooling layers to improve performance by continuously deepening the network. Compared with the previous excellent network structure, the error rate of this network model is lower. At the same time, the generalization ability of the network model is good, and it can have good performance on different image data sets, so the VGG network model is often used for feature extraction.In addition, VGGNet uses 3 × 3 convolution because convolution not only involves the amount of calculation but also affects the receptive field. The former is related to whether it is convenient to deploy to the mobile terminal, whether it can meet real-time processing, whether it is easy to train, etc. the latter is related to parameter update, the size of feature map, whether there are enough features extracted, model complexity, and parameter quantity, etc. It is noted that, under the same stripe, the characteristic graphs and convolution parameters of different convolution kernel sizes have little difference. The larger the convolution kernel, the greater the amount of computation.(2) ResNet Network Structure. The ResNet network proposes a connection method called short-circuit connection, which can solve the problem of gradient disappearance, as shown in Figure 2.Figure 2
Residual error module.As shown in Figure2, RESNET network introduces the residual module. Compared with the conventional nonlinear mapping, it also adds the identity mapping connected by jumping to obtain the potential mapping f (x) + X. For the network without jump connection, due to the phenomenon of gradient disappearance or gradient explosion, when the number of network layers is too many, backpropagation cannot be propagated to the shallow network. For the network with jump connection, in extreme cases, the nonlinear mapping is mapped to zero, and the expected potential mapping becomes an identity mapping, which is equivalent to “jumping” the middle structure. Once the residual mapping learns the effective parameters, it will increase the overall generalization ability of the residual network.In practice, the equivalent mapping function may not be so well optimized, but for residual learning, the solver will find disturbances more easily according to the input equivalent mapping. In short, it is much easier than learning an equivalent mapping function directly. According to the experiment, it can be found that the response value of the learned residual function is usually small, and the equivalent mapping provides a reasonable prerequisite.(3) Inception Network Structure. Inception improves the overall performance of the neural network by increasing the width of the network. The specific structure is shown in Figure 3.Figure 3
Inception module.By introducing the Inception module, the inception network structure connects the 1 × 1, 3 × 3, and 5 × 5 convolutional layer and the 3 × 3 pooling layer in parallel and finally superimposes the feature blocks of each branch and changes the traditional parallel structure is used.This paper optimizes on the basis of the algorithm based on deep learning and adds the idea of ResNet to the inception part so that the input feature map and the new feature map output by the convolutional layer are spliced, thereby increasing the diversity of features.The carefully designed inception module of inception net improves the performance of the model and reduces the amount of parameters. Generally speaking, to improve the expression ability of convolution network, it mainly depends on increasing the number of output channels, but the side effects are the increase of calculation and overfitting. Therefore, inception net uses branch networks stacked together to produce larger channel outputs. Because this is an excellent network, it can organize information across channels, improve the expression ability of the network, provide more nonlinear transformations, and have high cost performance. At the same time, the convolution and size in the network are also different, which can increase the adaptability of the network to different scales. Therefore, increasing the width and depth of the network by branching can improve the performance of the network and avoid overfitting.


## 2.1.1. Convolutional Neural Network

Convolutional neural networks are mainly composed of five different functional layers, which are input layer, convolution layer, pooling layer, fully connected layer, and output layer. According to different functions, convolutional neural networks can be divided into feature extractors and classifiers. Feature extractors mainly include input layer, convolution layer, and pooling layer, and the rest belong to classifiers [12]. The working principle of the feature extractor is that the convolutional layer separately calculates the feature map output after the activation function of the local features of the input image through the convolution calculation, and the classifier output after the feature map is compressed. The classifier is to integrate and map the extracted statistical features into the tag space of the sample and produce the output of the task. The basic structure of the convolutional neural network is shown in Figure 1.Figure 1
Basic structure of convolutional neural network.(1) Convolutional Layer. Convolution is the mathematical operation of two functions, that is, the integral of the product of one function g flipped and evaluated with the other function f, denoted as g∗f. The definition of continuous convolution is(1)f∗gx=∫fτgx−τdτ.Discrete convolution is defined as(2)f∗gn=∑m=−∞∞fmgn−m.(2) Pooling Layer. The pooling layer mainly performs corresponding statistical calculations on the adjacent output of a certain position and uses the result to represent the output of the network in that area. Another important function of the pooling operation is to pool the inputs of different sizes into a uniform size output.(3) Activation Layer. The function of the activation layer is mainly to perform nonlinear transformation on the input data. The convolutional layer without activation function is equivalent to matrix multiplication, which is a linear transformation. The activation function can add nonlinear factors to map the features to the nonlinear interval, so as to solve the problems that cannot be solved by the linear model. Commonly used activation functions are Sigmoid function, Tanh, ReLU function, and so on.(4) Fully Connected Layer. The fully connected layer mainly extracts the features of the previous convolutional layer and the pooling layer and composes more informative features. The parameters of the fully connected layer are mainly determined by the dimensions of the input and output and are not translation invariant.Top down supervised learning is to train through labeled data, transmit the error from top to bottom, and fine tune the network. Based on the parameters of each layer obtained in the first step, the parameters of the whole multilayer model are further optimized. This step is a supervised training process. The first step is similar to the random initialization initial value process of neural network. Because the first step is not random initialization, but obtained by learning the structure of input data, this initial value is closer to the global optimization, so it can achieve better results. Therefore, the good effect of deep learning is largely due to the first step of feature learning.


## 2.1.2. Design of Convolutional Neural Network

(1) VGGNet Network Structure. The VGGNet network model repeatedly stacks 3 × 3 small convolution kernels and 2 × 2 maximum pooling layers to improve performance by continuously deepening the network. Compared with the previous excellent network structure, the error rate of this network model is lower. At the same time, the generalization ability of the network model is good, and it can have good performance on different image data sets, so the VGG network model is often used for feature extraction.In addition, VGGNet uses 3 × 3 convolution because convolution not only involves the amount of calculation but also affects the receptive field. The former is related to whether it is convenient to deploy to the mobile terminal, whether it can meet real-time processing, whether it is easy to train, etc. the latter is related to parameter update, the size of feature map, whether there are enough features extracted, model complexity, and parameter quantity, etc. It is noted that, under the same stripe, the characteristic graphs and convolution parameters of different convolution kernel sizes have little difference. The larger the convolution kernel, the greater the amount of computation.(2) ResNet Network Structure. The ResNet network proposes a connection method called short-circuit connection, which can solve the problem of gradient disappearance, as shown in Figure 2.Figure 2
Residual error module.As shown in Figure2, RESNET network introduces the residual module. Compared with the conventional nonlinear mapping, it also adds the identity mapping connected by jumping to obtain the potential mapping f (x) + X. For the network without jump connection, due to the phenomenon of gradient disappearance or gradient explosion, when the number of network layers is too many, backpropagation cannot be propagated to the shallow network. For the network with jump connection, in extreme cases, the nonlinear mapping is mapped to zero, and the expected potential mapping becomes an identity mapping, which is equivalent to “jumping” the middle structure. Once the residual mapping learns the effective parameters, it will increase the overall generalization ability of the residual network.In practice, the equivalent mapping function may not be so well optimized, but for residual learning, the solver will find disturbances more easily according to the input equivalent mapping. In short, it is much easier than learning an equivalent mapping function directly. According to the experiment, it can be found that the response value of the learned residual function is usually small, and the equivalent mapping provides a reasonable prerequisite.(3) Inception Network Structure. Inception improves the overall performance of the neural network by increasing the width of the network. The specific structure is shown in Figure 3.Figure 3
Inception module.By introducing the Inception module, the inception network structure connects the 1 × 1, 3 × 3, and 5 × 5 convolutional layer and the 3 × 3 pooling layer in parallel and finally superimposes the feature blocks of each branch and changes the traditional parallel structure is used.This paper optimizes on the basis of the algorithm based on deep learning and adds the idea of ResNet to the inception part so that the input feature map and the new feature map output by the convolutional layer are spliced, thereby increasing the diversity of features.The carefully designed inception module of inception net improves the performance of the model and reduces the amount of parameters. Generally speaking, to improve the expression ability of convolution network, it mainly depends on increasing the number of output channels, but the side effects are the increase of calculation and overfitting. Therefore, inception net uses branch networks stacked together to produce larger channel outputs. Because this is an excellent network, it can organize information across channels, improve the expression ability of the network, provide more nonlinear transformations, and have high cost performance. At the same time, the convolution and size in the network are also different, which can increase the adaptability of the network to different scales. Therefore, increasing the width and depth of the network by branching can improve the performance of the network and avoid overfitting.


## 2.2. Construction of the Evaluation System for the Impact of Physical Indicators

### 2.2.1. The Goal of Building the System

(1)
Understanding the development status of school physical education and transforming physical indicators into operable measurement procedures and texts are important means to understand the development status of school physical education. Moreover, through a unified and homogeneous evaluation, it is possible to analyze the current situation and effectiveness of the implementation of school physical education and to provide a direction for improving education.(2)
Understanding the results of individual students’ physical education learning. We can understand how many learning results students have obtained in the whole process of physical education learning and provide a basis for the curriculum reform and teaching.


### 2.2.2. Principles of Constructing the System

(1)
The principle of scientificity: scientificity is the basis for achieving standardization and unification of parity indicators. The purpose of establishing a physical index impact evaluation system is to objectively reflect the level of students’ physical quality and analyze the main factors that affect the level of students’ physical quality so that teachers, parents, and students can clearly understand and improve their physical quality.(2)
Systematic principle: the system has the characteristics of purpose, relevance, hierarchy, and integrity, and the index system is a comprehensive body that contains rich information and comprehensively covers all aspects of the research object. When constructing indicators, it should be based on the needs of students, the development of knowledge, and the changes in society. It is necessary to pay attention to the convergence of the vertical system and the integration of the horizontal system.(3)
The guiding principle: the establishment of the index system is to achieve a certain purpose, which is convenient for comparison, analysis, and improvement. Operability is an important criterion for measuring the index.(4)
The principle of comprehensiveness: as a multifactor comprehensive evaluation, the indicators selected for the evaluation of students’ physical quality level should be comprehensive and try to avoid the appearance of excessively single or partial indicators, resulting in unreasonable evaluation results. When setting the initial indicators, the factors to choose should be more and more comprehensive so that it is more conducive to further optimization of the indicators. However, at the same time, attention should be paid to the establishment of indicators and the selection of data should be generalized, and the degree of repetition should not be too high.


### 2.2.3. Constructing the Framework of the Index System

The framework of constructing the index system is actually to determine the interrelationship and hierarchical structure between the indexes in the evaluation index system. The simplest form of the indicator structure is the two-layer structure and the three-layer structure. The two-layer structure mainly includes the overall target layer and the index layer. This structure does not classify the index structure. The three-tier structure includes the target layer, the subtarget layer, and the indicator layer. This article uses a three-tier structure.


### 2.2.4. Specific Steps to Construct System Indicators

(1) Theoretical Preparation. First of all, we must master the theories and methods of evaluation in education and sports and fully understand the current situation of students’ physical indicators and the problems and deficiencies in the evaluation standards, as well as the abilities that students should have in order to achieve the indicators, and systematically collect the elements of physical indicators evaluation. Establish a scientific and reasonable index system to make adequate preparations.(2) Primary Selection of Indicator System. Complete data collection, read relevant literature and books as required, make theoretical preparations, and have a certain foundation for research theories and methods. Then, through literature analysis, brainstorming and other methods are used to determine the single index. Finally, the analytic hierarchy process is used to construct the body index impact evaluation system.Besides, according to the nature of the problem and the overall goal to be achieved, the analytic hierarchy process decomposes the problem into different constituent factors and aggregates and combines the factors according to different levels according to the correlation, influence, and subordinate relationship between the factors to form a multilevel analysis structure model, so as to finally reduce the problem to the lowest level (scheme for decision-making measures, etc.) determination of relatively important weights relative to the highest level (overall goal) or arrangement of relative advantages and disadvantages.(3) Test of Indicator System. For the system that was established for the first time, the Delphi method was used and experts were asked to conduct two rounds of tests, and then, the indicator system was optimized to make the entire system more complete and more reasonable.(4) Index Weight Distribution of the Index System. The index weight distribution of the index system is the key. The relevant experts in physical education research are selected, and the analytic hierarchy process is used to distribute the index weights, hoping that the distribution of index weights can truly reflect the characteristics of students’ sports quality and the sports that need to be strengthened.


## 2.2.1. The Goal of Building the System

(1)
Understanding the development status of school physical education and transforming physical indicators into operable measurement procedures and texts are important means to understand the development status of school physical education. Moreover, through a unified and homogeneous evaluation, it is possible to analyze the current situation and effectiveness of the implementation of school physical education and to provide a direction for improving education.(2)
Understanding the results of individual students’ physical education learning. We can understand how many learning results students have obtained in the whole process of physical education learning and provide a basis for the curriculum reform and teaching.


## 2.2.2. Principles of Constructing the System

(1)
The principle of scientificity: scientificity is the basis for achieving standardization and unification of parity indicators. The purpose of establishing a physical index impact evaluation system is to objectively reflect the level of students’ physical quality and analyze the main factors that affect the level of students’ physical quality so that teachers, parents, and students can clearly understand and improve their physical quality.(2)
Systematic principle: the system has the characteristics of purpose, relevance, hierarchy, and integrity, and the index system is a comprehensive body that contains rich information and comprehensively covers all aspects of the research object. When constructing indicators, it should be based on the needs of students, the development of knowledge, and the changes in society. It is necessary to pay attention to the convergence of the vertical system and the integration of the horizontal system.(3)
The guiding principle: the establishment of the index system is to achieve a certain purpose, which is convenient for comparison, analysis, and improvement. Operability is an important criterion for measuring the index.(4)
The principle of comprehensiveness: as a multifactor comprehensive evaluation, the indicators selected for the evaluation of students’ physical quality level should be comprehensive and try to avoid the appearance of excessively single or partial indicators, resulting in unreasonable evaluation results. When setting the initial indicators, the factors to choose should be more and more comprehensive so that it is more conducive to further optimization of the indicators. However, at the same time, attention should be paid to the establishment of indicators and the selection of data should be generalized, and the degree of repetition should not be too high.


## 2.2.3. Constructing the Framework of the Index System

The framework of constructing the index system is actually to determine the interrelationship and hierarchical structure between the indexes in the evaluation index system. The simplest form of the indicator structure is the two-layer structure and the three-layer structure. The two-layer structure mainly includes the overall target layer and the index layer. This structure does not classify the index structure. The three-tier structure includes the target layer, the subtarget layer, and the indicator layer. This article uses a three-tier structure.


## 2.2.4. Specific Steps to Construct System Indicators

(1) Theoretical Preparation. First of all, we must master the theories and methods of evaluation in education and sports and fully understand the current situation of students’ physical indicators and the problems and deficiencies in the evaluation standards, as well as the abilities that students should have in order to achieve the indicators, and systematically collect the elements of physical indicators evaluation. Establish a scientific and reasonable index system to make adequate preparations.(2) Primary Selection of Indicator System. Complete data collection, read relevant literature and books as required, make theoretical preparations, and have a certain foundation for research theories and methods. Then, through literature analysis, brainstorming and other methods are used to determine the single index. Finally, the analytic hierarchy process is used to construct the body index impact evaluation system.Besides, according to the nature of the problem and the overall goal to be achieved, the analytic hierarchy process decomposes the problem into different constituent factors and aggregates and combines the factors according to different levels according to the correlation, influence, and subordinate relationship between the factors to form a multilevel analysis structure model, so as to finally reduce the problem to the lowest level (scheme for decision-making measures, etc.) determination of relatively important weights relative to the highest level (overall goal) or arrangement of relative advantages and disadvantages.(3) Test of Indicator System. For the system that was established for the first time, the Delphi method was used and experts were asked to conduct two rounds of tests, and then, the indicator system was optimized to make the entire system more complete and more reasonable.(4) Index Weight Distribution of the Index System. The index weight distribution of the index system is the key. The relevant experts in physical education research are selected, and the analytic hierarchy process is used to distribute the index weights, hoping that the distribution of index weights can truly reflect the characteristics of students’ sports quality and the sports that need to be strengthened.


## 2.3. Database Establishment

Note that the data processing method approximately describes or compares the functional relationship between the coordinates represented by the discrete point group on the plane with the continuous curve. A common method is to use analytical expressions to approximate discrete data. Curve linearization is one of the important means of curve fitting. For some nonlinear data, it can be linearized by simple variable transformation so that the linear equation of the transformed variable can be obtained according to the principle of the least square method.


### 2.3.1. Sports Training Database

In order to study the impact of physical training on physical indicators, a database of the impact of physical training on physical indicators is constructed based on real data. The data resources of the database come from the actual measurement of 400 college students majoring in Physical Education in a university in our province. Firstly, according to physical training, it is divided into wrestling, foot racing, and skills. In addition, in order to reflect the impact of different sports on physical indicators, this paper also adds nonsports. Therefore, the data used in this paper are divided into four categories: wrestling, tug of war, and wrist breaking. Foot racing mainly includes rope skipping, eagle catching chicken, football, and running. Skill sports mainly include rubber band jumping, throwing sandbags, shuttlecock kicking, and house jumping.


### 2.3.2. Physical Indicators

There are 24 physical indicators in the sports training database, which are divided into three categories. The first is the physical shape indicators, which mainly include height, weight, chest circumference, waist circumference, hip circumference, shoulder width, sitting height, pelvic width, and body fat percentage; the tightness of the upper hip is poor. Physical function indicators mainly include vital capacity, basal heart rate, maximum oxygen uptake, heart function index, and pulse pressure difference. The last category is sports quality indicators, one includes standing long jump, push-ups, sit-ups, 50 m sprints, sitting forward bending, grip strength, cross running, back-and-forth running, and back muscle strength.


### 2.3.3. Training Settings

Students are divided into 4 groups, each corresponding to a category. First, each group will be tested for the physical index mark before training, and then, a dedicated person will guide each group to conduct the corresponding training. They will exercise 4 times a week, each exercise for 30 minutes, and then add with a preparation time of 7 minutes and an end time of 3 minutes; the experiment time is 2 months. The body index data before training are denoted as A1, and the body index data after training are denoted as A2. Table1 shows some of the data.Table 1
Examples of database measurement data.
Person123A1A2A1A2A1A2Height (cm)158.1158.2167.8167.8187.4187.5Sitting height (cm)82.582.692.592.5110.4110.450 m sprint (s)9.49.198.88.48.2Average grip strength (kg)21.526.226.528.430.432.5Back muscle strength (kg)57.160.247.555.876.879.5


### 2.3.4. Ranking of the Degree of Influence of Physical Indicators

After performing data cleaning and data preprocessing on the collected data, a total of 400 people’s body index change data are obtained and then classified and labeled and further obtain the sorting formula of the real influence degree of sports training on the body index, as shown in(3)ImpactNxa=∑m=1zvotem,xa.Among them,ImpactNxa represents the importance of the Nth type of sports to the ath physical index, m represents the mth voter, and z represents the total number of voters.Then, experts are asked to rank and vote on the respective influence levels of the 24 indicators that have been measured for wrestling, competition, and technical sports, to obtain an orderly sequence of the top 5 physical indicators with significant impacts of the three types of sports and construct physical indicators for sports. The actual degree of influence of the impact will eventually form a database of the physical index impact of sports training. The results are shown in Table2.Table 2
The true ranking of the impact of various sports on physical indicators.
Wrestling sportsRace foot sportsAcrobatic sports1Back muscle strengthBack and forthHeart function index2GripCross direction run50 m sprint3Poor upper hip tightness50 m sprintCross direction run4Heart function indexStanding long jumpBack and forth5Poor breathingHeart function indexSit-ups


## 2.3.1. Sports Training Database

In order to study the impact of physical training on physical indicators, a database of the impact of physical training on physical indicators is constructed based on real data. The data resources of the database come from the actual measurement of 400 college students majoring in Physical Education in a university in our province. Firstly, according to physical training, it is divided into wrestling, foot racing, and skills. In addition, in order to reflect the impact of different sports on physical indicators, this paper also adds nonsports. Therefore, the data used in this paper are divided into four categories: wrestling, tug of war, and wrist breaking. Foot racing mainly includes rope skipping, eagle catching chicken, football, and running. Skill sports mainly include rubber band jumping, throwing sandbags, shuttlecock kicking, and house jumping.


## 2.3.2. Physical Indicators

There are 24 physical indicators in the sports training database, which are divided into three categories. The first is the physical shape indicators, which mainly include height, weight, chest circumference, waist circumference, hip circumference, shoulder width, sitting height, pelvic width, and body fat percentage; the tightness of the upper hip is poor. Physical function indicators mainly include vital capacity, basal heart rate, maximum oxygen uptake, heart function index, and pulse pressure difference. The last category is sports quality indicators, one includes standing long jump, push-ups, sit-ups, 50 m sprints, sitting forward bending, grip strength, cross running, back-and-forth running, and back muscle strength.


## 2.3.3. Training Settings

Students are divided into 4 groups, each corresponding to a category. First, each group will be tested for the physical index mark before training, and then, a dedicated person will guide each group to conduct the corresponding training. They will exercise 4 times a week, each exercise for 30 minutes, and then add with a preparation time of 7 minutes and an end time of 3 minutes; the experiment time is 2 months. The body index data before training are denoted as A1, and the body index data after training are denoted as A2. Table1 shows some of the data.Table 1
Examples of database measurement data.
Person123A1A2A1A2A1A2Height (cm)158.1158.2167.8167.8187.4187.5Sitting height (cm)82.582.692.592.5110.4110.450 m sprint (s)9.49.198.88.48.2Average grip strength (kg)21.526.226.528.430.432.5Back muscle strength (kg)57.160.247.555.876.879.5


## 2.3.4. Ranking of the Degree of Influence of Physical Indicators

After performing data cleaning and data preprocessing on the collected data, a total of 400 people’s body index change data are obtained and then classified and labeled and further obtain the sorting formula of the real influence degree of sports training on the body index, as shown in(3)ImpactNxa=∑m=1zvotem,xa.Among them,ImpactNxa represents the importance of the Nth type of sports to the ath physical index, m represents the mth voter, and z represents the total number of voters.Then, experts are asked to rank and vote on the respective influence levels of the 24 indicators that have been measured for wrestling, competition, and technical sports, to obtain an orderly sequence of the top 5 physical indicators with significant impacts of the three types of sports and construct physical indicators for sports. The actual degree of influence of the impact will eventually form a database of the physical index impact of sports training. The results are shown in Table2.Table 2
The true ranking of the impact of various sports on physical indicators.
Wrestling sportsRace foot sportsAcrobatic sports1Back muscle strengthBack and forthHeart function index2GripCross direction run50 m sprint3Poor upper hip tightness50 m sprintCross direction run4Heart function indexStanding long jumpBack and forth5Poor breathingHeart function indexSit-ups


## 3. Experimental Test

### 3.1. Experimental Setup

Experiments are carried out on the basis of the database established in this paper. The database settings are divided into the training set and the test set. The other three baseline algorithms and the algorithm designed in this paper are used for comparative experiments. This article mainly studies the impact of deep learning-based sports training on changes in physical indicators, so the data obtained in sports training experiments are regarded as a positive category, and other data are regarded as a negative category.In the fields of machine learning and pattern recognition, it is generally necessary to divide the samples into three independent parts: training set, verification set, and test set. The training set is used to estimate the model, the verification set is used to determine the network structure or the parameters controlling the complexity of the model, and the test set is used to test the performance of the finally selected optimal model. A typical division is that the training set accounts for 50% of the total samples, while the others account for 25%, respectively. The three parts are randomly selected from the samples.


### 3.2. Evaluation Index

This paper mainly uses deep learning to select behavior effects. There are four situations for the detection results and real annotation of the classifier. The first is the correct positive class (TP), which means that it is actually a positive class, and the detection result is also a positive class. The second case is the error positive class (FP), which means that it is actually a negative class and the detection result is a positive class. The third case is the correct negative class (TN), which means that it is actually a negative class and the detection result is also a negative class. The last case is the error negative class (FN), which means that it is actually a positive class and the detection result is indeed a negative class. Therefore, the accuracy is the proportion of the actual correct number of classifiers in the positive class. Recall rate represents the full rate, which indicates the proportion of the actual correct number of classifiers in all positive classes. Target detection generally uses the average detection accuracy of all types, which represents the average value of the average accuracy of each type.


## 3.1. Experimental Setup

Experiments are carried out on the basis of the database established in this paper. The database settings are divided into the training set and the test set. The other three baseline algorithms and the algorithm designed in this paper are used for comparative experiments. This article mainly studies the impact of deep learning-based sports training on changes in physical indicators, so the data obtained in sports training experiments are regarded as a positive category, and other data are regarded as a negative category.In the fields of machine learning and pattern recognition, it is generally necessary to divide the samples into three independent parts: training set, verification set, and test set. The training set is used to estimate the model, the verification set is used to determine the network structure or the parameters controlling the complexity of the model, and the test set is used to test the performance of the finally selected optimal model. A typical division is that the training set accounts for 50% of the total samples, while the others account for 25%, respectively. The three parts are randomly selected from the samples.


## 3.2. Evaluation Index

This paper mainly uses deep learning to select behavior effects. There are four situations for the detection results and real annotation of the classifier. The first is the correct positive class (TP), which means that it is actually a positive class, and the detection result is also a positive class. The second case is the error positive class (FP), which means that it is actually a negative class and the detection result is a positive class. The third case is the correct negative class (TN), which means that it is actually a negative class and the detection result is also a negative class. The last case is the error negative class (FN), which means that it is actually a positive class and the detection result is indeed a negative class. Therefore, the accuracy is the proportion of the actual correct number of classifiers in the positive class. Recall rate represents the full rate, which indicates the proportion of the actual correct number of classifiers in all positive classes. Target detection generally uses the average detection accuracy of all types, which represents the average value of the average accuracy of each type.


## 4. Analysis of Experimental Results

### 4.1. Impact of Different Types of Sports on Physical Indicators

First, analyze the wrestling sports, and the results are shown in Figure4.Figure 4
The 10 most influential physical indicators in wrestling sports.It can be seen from Figure4 that, for wrestling sports, the most influential factors are the average grip strength, heart performance index, one-minute sit-ups, waist circumference, and 50 m sprint. The main exercise of wrestling is strength, and the result is consistent with general cognition. For racing foot sports, waist circumference has the greatest impact, with a degree of impact reaching 0.82, followed by cardio work index, 50 m sprint, standing long jump, round-trip running, and body weight. Competitive foot sports mainly exercise the reaction ability of bodily functions, especially the lower body of the body. For technical sports, waist circumference has the greatest impact, with an impact degree of 0.76, followed by 50 m sprint, cardio index, weight, and cross direction running. The technical exercise mainly exercises the reaction force, and the result is consistent with general cognition.


### 4.2. Analysis of Comparative Experiment Results

The deep learning algorithm (DLA) proposed in this paper is compared with the mean, variance, and recursive feature elimination algorithm (RFE). The results are shown in Figure5.Figure 5
Comparison of classification performance of four algorithms.It can be seen from Figure5 that the accuracy of the algorithm proposed in this paper is significantly higher than that of the other three algorithms. First, in wrestling sports, the accuracy of the mean algorithm is 0.4, the accuracy of the variance is 0.2, the accuracy of the RFE algorithm is 0.4, and the algorithm of DLA is 0.6; it is more obvious in competition sports; the accuracy is 0.2, 0, 0.2, and 0.6, respectively; in technical sports, the accuracy is 0.4, 0.2, 0.2, and 0.8, respectively. It can be explained that the method proposed in this article is more effective in evaluating the impact of physical training on physical indicators.


## 4.1. Impact of Different Types of Sports on Physical Indicators

First, analyze the wrestling sports, and the results are shown in Figure4.Figure 4
The 10 most influential physical indicators in wrestling sports.It can be seen from Figure4 that, for wrestling sports, the most influential factors are the average grip strength, heart performance index, one-minute sit-ups, waist circumference, and 50 m sprint. The main exercise of wrestling is strength, and the result is consistent with general cognition. For racing foot sports, waist circumference has the greatest impact, with a degree of impact reaching 0.82, followed by cardio work index, 50 m sprint, standing long jump, round-trip running, and body weight. Competitive foot sports mainly exercise the reaction ability of bodily functions, especially the lower body of the body. For technical sports, waist circumference has the greatest impact, with an impact degree of 0.76, followed by 50 m sprint, cardio index, weight, and cross direction running. The technical exercise mainly exercises the reaction force, and the result is consistent with general cognition.


## 4.2. Analysis of Comparative Experiment Results

The deep learning algorithm (DLA) proposed in this paper is compared with the mean, variance, and recursive feature elimination algorithm (RFE). The results are shown in Figure5.Figure 5
Comparison of classification performance of four algorithms.It can be seen from Figure5 that the accuracy of the algorithm proposed in this paper is significantly higher than that of the other three algorithms. First, in wrestling sports, the accuracy of the mean algorithm is 0.4, the accuracy of the variance is 0.2, the accuracy of the RFE algorithm is 0.4, and the algorithm of DLA is 0.6; it is more obvious in competition sports; the accuracy is 0.2, 0, 0.2, and 0.6, respectively; in technical sports, the accuracy is 0.4, 0.2, 0.2, and 0.8, respectively. It can be explained that the method proposed in this article is more effective in evaluating the impact of physical training on physical indicators.


## 5. Conclusions

In recent years, with the emergence of big data and the significant improvement of computer parallel computing ability, deep learning has made a breakthrough in the field of computer vision and natural language processing by relying on rich training data and strong feature expression ability. Based on deep learning technology, this paper puts forward a method to evaluate the impact of physical training on body finger rebased on deep learning. As the main force of national reserve talents, the students’ physical health level should be guaranteed. In order to improve the quality and efficiency of physical education, give more entry points, provide more choices for effectively intervening in students’ physical health mechanism, and make suggestions for scientifically promoting the research on students’ physical exercise, so as to finally achieve the purpose of improving students’ physique, this paper evaluates the impact of physical indicators and accurately grasps the interaction degree between variables, combining with in-depth learning technology.



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*Source: 6924262-2021-09-29.xml*