# College Sports Multimedia Network Based on Wireless Communication and Deep Learning Network Environment

Authors: Shihong Liu; Qiao Chen
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

At this stage, network information technology has been fully popularized and widely used in various fields and has achieved remarkable results. In the development of physical education courses in colleges and universities, the effective use of multimedia and network-assisted teaching can greatly improve the physical quality of college students, stimulate students’ interest in physical education courses, and promote students’ all-round development. Therefore, after the teaching is over, comparative analysis is used to compare the teaching effects of the computer network educational pattern and the previous educational pattern to determine the suitability of the university sports multimedia network system. The experiment proved that the fertility and health test scores of the reference team have been partially improved (the fertility and health test scores of the reference team increased from 3.05 points to 3.87 points), and the fertility and health test scores of the experimental group students have been greatly improved (the fertility and health test scores of the experimental group increased from 3.24 points to 5.58 points), which shows that the multimedia network teaching has broken passing the constraints of times and spatial limitations. Students can learn sports knowledge even in extracurricular time. It complements the traditional teaching methods of precepts and deeds and is the development trend of modern physical education.


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## Body

## 1. Introduction

Modern distance education owns a broad prospect for the leap-forward progress of teaching in our country. Various new types of online universities, virtual universities, and online education bases have gradually formed. The formation and development of contemporary remote training has been a major portion of contemporary training, and it also reflects the development process of modern science and technology and its application in the field of education. Make use of the timeliness, interaction, and displayability of multimedia networks to provide online learning guidance and help more college students and nonschool sports enthusiasts. Spread of e-learning in university physical teaching has broken through the traditional geographical restrictions. It is not limited to schools. We can also bring sports learning to families, communities, and even any place with network coverage.With the development of information technology, the application of multimedia network teaching platform is becoming more and more extensive. In college physical education, the design and development of network environment based on wireless communication and deep learning has become more and more mature, which can effectively help college students learn physical knowledge and master physical skills. The rational use of modern information technology, the introduction of the Internet into traditional teaching, and the construction of a teaching system with powerful functions and reasonable structure are receiving widespread attention. The wireless voting method can quickly process a large amount of information and shorten the voting process time. Using classroom wireless voting can help teachers grasp the students’ classroom cognition and can appropriately change the teaching progress and goals based on the feedback information.The time for teaching through multimedia network in abroad is much sooner than domestic, rapid updates of network teaching techniques in multimedia. The teaching methods have been greatly developed. It is believed that in the near future, the teaching assisted by the network and multimedia technology will become an indispensable link in language teaching. Munoz-Organero et al. identify the present state of affairs of the construction of modern physical education professional online courses in our country through the use of online surveys. It points out that although the learning resources of the websites of various universities are rich, they are not organically integrated with the content of the courses. The design level and the evaluation and feedback of the problem need to be improved [1]. Peng et al. found and pointed out that the development of multimedia network teaching will change the traditional competitive sports-centered system and the teaching work will be more interesting and practical [2]. Li et al. pointed out that it is important to design a new idea to build a networked learning society, while improving the information literacy of educators and educators of multimedia network teaching, and to strengthen the construction and research of sports network education [3]. With sustainable evolution of information communication and the maturity of computer network technique, the multimedia network teaching system can expand the activity space and practice field of physical education. Hu et al. pointed out that the utility of appropriate multimedia technology to increase productivity of sports network teaching and the establishment of school-to-school information resource sharing broaden the bridge of physical education and improve and perfect the quality of sports information [4]. Nam et al. pointed out that the development of multimedia network teaching in our country has a good policy background and the development of related software and hardware systems is also improving day by day, which is conducive to the training of new sports talents [5]. Liao et al. pointed out that modern computer network science and industry have given a fresh perspective to university physical training, and the use of such technology in PE teaching is an important part of inheriting and evolving conventional PE teaching [6]. Although these studies have conducted a more in-depth discussion on the connotation of multimedia network teaching as a teaching method, there are relatively few studies on the application of multimedia network teaching. At present, the development of deep learning networks and wireless communication technologies can provide greater impetus for the research of multimedia physical education teaching and expand research perspectives.The research and problem of this article is to show the merits of interactive web teaching in teaching sport and summarize and analyze how the multimedia network teaching breaks through the limitations of traditional college physical education and how to promote the multimedia network teaching to universities at the same time; it makes full use of the resource sharing function of the Internet to effectively and rationally integrate the resource information of the multimedia network teaching of various universities and various sports websites to achieve the purpose of complementary advantages and break the traditional physical education teaching space. With time constraints, the optimization of teaching resources is realized. Combining high-stability wireless communication technology with embedded technology with strong specificity and high practicability, a set of intelligent physical education based on wireless communication is researched and built.


## 2. College Sports Multimedia Network System Based on Deep Learning

### 2.1. Deep Learning

The research on convolutional neural networks began in the 1980s and 1990s, and the time delay network and LeNet-5 were the earliest convolutional neural networks; after the 21st century, with the introduction of deep learning theory and numerical computing with the improvement of equipment, the convolutional neural network has been developed rapidly and has been applied to the fields of computer vision, natural language processing, and so on.


#### 2.1.1. Basic Structure of Deep Convolutional Nervous Nets

The use of neuron shared weights greatly reduces the amount of parameters that need to be trained in the neural network. In addition, the shared weight strategy used can be applied to achieve anterior spreading of nerve grids by convolution on the image data. Connected after the deconvolution stage is the pooling layer, which is used for nonlinear downsampling [7, 8]. Between the convolution and pooling layers is the activation function, which mimics the neuron’s response to input. After the convolution and pooling layer, the characteristic output of deconvolution evaluation and then the full link layer is connected.(1) Full Link Layer. Generally speaking, the channel refers to the color channel of the picture, and the feature map is the output result of the convolution filter. But in fact, the two are essentially the same; both are data representing the distribution of a certain feature on the previous input. In the fully linked stage of the nervous nets, when understanding the output of a layer, the weight vector used by each neuron of the output is not the same. The weighted input and output calculation formulas of the l layer are shown:
(1)zjl=∑k=03wjklakl−1+bjl,j=0,1,(2)ajl=σzjl,j=0,1,where zjl represents the weighted input of the j neuron in the l stage, ajl represents the output of the j nerve elements in the l stage, bjl shows the bias of the j nerve elements in the l layer, σzjl represents the activation function, and wjkl shows the connection weight from the k nerve elements in the l−1 stage to the j nerve elements in the k stage [9, 10].(2) Convolutional Layer. When performing convolution operations in image processing, the convolution kernel needs to be flipped 180 degrees first. The two-dimensional discrete convolution operation formula in image processing is shown:
(3)Si,j=W∗Ii,j=∑s∑tWi,tIi−s,j−t,where I represents the input image, W means the convolution, and S means the output of the convolution operation. Unlike image processing, the so-called convolution in deep neural networks is actually a correlation function, or cross-correlation function, as shown in
(4)Sa,b=W∗Ia,b=∑s∑tWs,tIa+s,i+t.This formula is almost the same as the convolution operation formula (3), but the core W is not flipped. In neuroscience, the local receptive field means that a neuron can receive the signal output of other upper neurons from different locations and a certain spatial range and integrate them as its own input [11, 12]. The local receptive field of the convolutional neural network is similar to this concept, and the concept of shared weight is introduced. The weighted input and output calculation formulas of the first layer can be expressed by
(5)zx,yl=∑s=02∑t=02ws,tlax+s,y+tl−1+bl,(6)ax,yl=σzx,yl,x,y=0,1,where zx,yl represents the weighted input of the nerve elements in the l layer, ax,yl represents the output of the neurons in the l layer, bl means the shared bias of the nerve elements in the l layer, and σzx,yl means the activation function. It is shown in equations (7), (8), and (9), respectively. At present, the most used activation features in deeply involutional nets is the ReLu feature.
(7)σzx,yl=11+e−zx,yl,(8)σzx,yl=ezx,yl−e−zx,ylezx,yl+e−zx,yl,(9)σzx,yl=max0,zx,yl.Compared with full link, the design of partial receptive field reduces the amount of calculation during forward propagation. Although the design of shared weights does not reduce the forward propagation time, it significantly reduces the need for neural network parameter storage [13]. Weight sharing means that, given an input image, use a convolution kernel to scan the image. The number in the convolution kernel is called the weight. Each position of this image is scanned by the same convolution kernel, so the weights are the same, that is, shared.(3) Pooling Stage. Iked level is typically applied to decrease the number of dimensions of the characteristic masking but to a certain extent retains the features of the original data. The pooling function uses the feature to map the statistical features in a certain location field to replace the feature of the location [14].(4) Loss Function. Generally in neural networks, softmax can be used as the output layer for classification tasks. In fact, it can be considered that softmax outputs the probabilities of several categories. For example, I have a classification task that needs to be divided into three categories. The softmax function can output the probabilities of three categories according to their relative sizes, and the sum of the probabilities is 1. Assuming that the softmax classification is performed on the L layer of the neural network, the L layer has a total of k neurons, and the weighted input and output calculation formulas of the L layer are shown [15, 16]:
(10)zjL=∑kwjkLakL−1+bjL,(11)ajL=ezjL∑kezkL,where zjL represents the weighted input of layer L neurons, ajL represents the output of layer L neurons, and akL−1 represents the output of layer L neurons. From equation (11), the sum of the outputs of all neurons in layer L is equal to 1, that is,
(12)∑j=1kajL=1.The softmax loss function formula is shown:(13)C=−1m∑i=1m∑j=1k1yi=jlogajL.This formula is the loss function ofm samples, and yi represents the label of the i sample. The value range of 1yi=j is only 0 and 1. When the brace equality is established, it is 1, and when it is not, it is 0. It can be seen from equation (13) that the loss function formula of a single sample xi is shown in
(14)Cxi=−logayiL.


#### 2.1.2. Gradient Descent

In deep neural networks, the purpose of optimizingC is to find the variable v1,v2,⋯,vn to minimize the value of the loss function. From the knowledge of calculus, when v changes by a small increment, the increment of the loss function C can be expressed by its total differential, as shown in
(15)ΔC≈∂C∂v1Δv1+∂C∂v2Δv2+⋯+∂C∂vnΔvn.ΔC can be written as follows:
(16)ΔC≈∇C⋅Δv,(17)∇C=∂C∂v1,∂C∂v2,⋯,∂C∂vnT,(18)Δv=Δv1,Δv2,⋯,ΔvnT.SupposeΔv is as shown in
(19)Δv=−η∇C,(20)ΔC=−η∇C2,where ∇C2≥0 guarantees ΔC≤0. If v changes according to the rules in equation (19), then the loss function C will always decrease and not increase [17].


### 2.2. Network Multimedia Teaching

#### 2.2.1. The Definition of Network Multimedia Teaching

Deep learning is to learn the inherent laws and representation levels of sample data, and the information obtained during these learning processes is of great help to the interpretation of data such as text, images, and sounds. Its ultimate goal is to enable machines to have the ability to analyze and learn like humans and to recognize data such as words, images, and sounds. Deep learning is a complex machine learning algorithm that has achieved results in speech and image recognition far exceeding previous related technologies. There is no standardized version of the defense of online multimedia instruction. I define online multimedia teaching as follows: the supposed online multimedia teaching is a new approach to teaching by integrating the most innovative computer network and multimedia technologies completely [18, 19]; it is the application of multimedia technology which is supported by the Internet that will be boring. Through cognitive computing network technique, the boring information in the lecture room is closely mixed into the audio, video, movie, and movements to enable a teaching method based on exchanges and debates.


#### 2.2.2. Appearance of Online Multilateral Teaching

With the mainstream development of medial and IT, online multimedia teaching is the third age of distance study. To a degree, network multimedia teaching is a derivative of the innovation of modern day computer network technique and multimedia technology. With the existence and development of computer network techniques and multimedia technologies, bidirectional interactive electric information communications are realized [20–23]. This allows the process of education to be much more overt and flexible. Therefore, with the advent and development of new techniques, network multimedia education emerged.


#### 2.2.3. Status Quo of the Development of Multimedia Network Education

Take as a foundation the construction of computer networks in our country and the construction of educational resources as an important factor in the implementation of education. With the construction of campus network infrastructure, the spread and development of the Internet, the constant innovation of multimedia technology, and the large educational population in China, many reasons have attracted many in-house software development companies to turn to the development of multimedia software education networks. In short, the construction of multimedia network education is fully integrated with the support of the local area and all social strata [24, 25].


#### 2.2.4. Features of Online Multimedia Teaching

Online multimedia teaching uses modern Internet technology and multimedia technology to break the constraints of traditional time and teaching space. Learners can start their own businesses anytime and anywhere. If they need to use computer equipment through the Internet connection and access the multimedia teaching network, they can choose the courses that suit them and easily obtain the latest and most complete information.


## 2.1. Deep Learning

The research on convolutional neural networks began in the 1980s and 1990s, and the time delay network and LeNet-5 were the earliest convolutional neural networks; after the 21st century, with the introduction of deep learning theory and numerical computing with the improvement of equipment, the convolutional neural network has been developed rapidly and has been applied to the fields of computer vision, natural language processing, and so on.


### 2.1.1. Basic Structure of Deep Convolutional Nervous Nets

The use of neuron shared weights greatly reduces the amount of parameters that need to be trained in the neural network. In addition, the shared weight strategy used can be applied to achieve anterior spreading of nerve grids by convolution on the image data. Connected after the deconvolution stage is the pooling layer, which is used for nonlinear downsampling [7, 8]. Between the convolution and pooling layers is the activation function, which mimics the neuron’s response to input. After the convolution and pooling layer, the characteristic output of deconvolution evaluation and then the full link layer is connected.(1) Full Link Layer. Generally speaking, the channel refers to the color channel of the picture, and the feature map is the output result of the convolution filter. But in fact, the two are essentially the same; both are data representing the distribution of a certain feature on the previous input. In the fully linked stage of the nervous nets, when understanding the output of a layer, the weight vector used by each neuron of the output is not the same. The weighted input and output calculation formulas of the l layer are shown:
(1)zjl=∑k=03wjklakl−1+bjl,j=0,1,(2)ajl=σzjl,j=0,1,where zjl represents the weighted input of the j neuron in the l stage, ajl represents the output of the j nerve elements in the l stage, bjl shows the bias of the j nerve elements in the l layer, σzjl represents the activation function, and wjkl shows the connection weight from the k nerve elements in the l−1 stage to the j nerve elements in the k stage [9, 10].(2) Convolutional Layer. When performing convolution operations in image processing, the convolution kernel needs to be flipped 180 degrees first. The two-dimensional discrete convolution operation formula in image processing is shown:
(3)Si,j=W∗Ii,j=∑s∑tWi,tIi−s,j−t,where I represents the input image, W means the convolution, and S means the output of the convolution operation. Unlike image processing, the so-called convolution in deep neural networks is actually a correlation function, or cross-correlation function, as shown in
(4)Sa,b=W∗Ia,b=∑s∑tWs,tIa+s,i+t.This formula is almost the same as the convolution operation formula (3), but the core W is not flipped. In neuroscience, the local receptive field means that a neuron can receive the signal output of other upper neurons from different locations and a certain spatial range and integrate them as its own input [11, 12]. The local receptive field of the convolutional neural network is similar to this concept, and the concept of shared weight is introduced. The weighted input and output calculation formulas of the first layer can be expressed by
(5)zx,yl=∑s=02∑t=02ws,tlax+s,y+tl−1+bl,(6)ax,yl=σzx,yl,x,y=0,1,where zx,yl represents the weighted input of the nerve elements in the l layer, ax,yl represents the output of the neurons in the l layer, bl means the shared bias of the nerve elements in the l layer, and σzx,yl means the activation function. It is shown in equations (7), (8), and (9), respectively. At present, the most used activation features in deeply involutional nets is the ReLu feature.
(7)σzx,yl=11+e−zx,yl,(8)σzx,yl=ezx,yl−e−zx,ylezx,yl+e−zx,yl,(9)σzx,yl=max0,zx,yl.Compared with full link, the design of partial receptive field reduces the amount of calculation during forward propagation. Although the design of shared weights does not reduce the forward propagation time, it significantly reduces the need for neural network parameter storage [13]. Weight sharing means that, given an input image, use a convolution kernel to scan the image. The number in the convolution kernel is called the weight. Each position of this image is scanned by the same convolution kernel, so the weights are the same, that is, shared.(3) Pooling Stage. Iked level is typically applied to decrease the number of dimensions of the characteristic masking but to a certain extent retains the features of the original data. The pooling function uses the feature to map the statistical features in a certain location field to replace the feature of the location [14].(4) Loss Function. Generally in neural networks, softmax can be used as the output layer for classification tasks. In fact, it can be considered that softmax outputs the probabilities of several categories. For example, I have a classification task that needs to be divided into three categories. The softmax function can output the probabilities of three categories according to their relative sizes, and the sum of the probabilities is 1. Assuming that the softmax classification is performed on the L layer of the neural network, the L layer has a total of k neurons, and the weighted input and output calculation formulas of the L layer are shown [15, 16]:
(10)zjL=∑kwjkLakL−1+bjL,(11)ajL=ezjL∑kezkL,where zjL represents the weighted input of layer L neurons, ajL represents the output of layer L neurons, and akL−1 represents the output of layer L neurons. From equation (11), the sum of the outputs of all neurons in layer L is equal to 1, that is,
(12)∑j=1kajL=1.The softmax loss function formula is shown:(13)C=−1m∑i=1m∑j=1k1yi=jlogajL.This formula is the loss function ofm samples, and yi represents the label of the i sample. The value range of 1yi=j is only 0 and 1. When the brace equality is established, it is 1, and when it is not, it is 0. It can be seen from equation (13) that the loss function formula of a single sample xi is shown in
(14)Cxi=−logayiL.


### 2.1.2. Gradient Descent

In deep neural networks, the purpose of optimizingC is to find the variable v1,v2,⋯,vn to minimize the value of the loss function. From the knowledge of calculus, when v changes by a small increment, the increment of the loss function C can be expressed by its total differential, as shown in
(15)ΔC≈∂C∂v1Δv1+∂C∂v2Δv2+⋯+∂C∂vnΔvn.ΔC can be written as follows:
(16)ΔC≈∇C⋅Δv,(17)∇C=∂C∂v1,∂C∂v2,⋯,∂C∂vnT,(18)Δv=Δv1,Δv2,⋯,ΔvnT.SupposeΔv is as shown in
(19)Δv=−η∇C,(20)ΔC=−η∇C2,where ∇C2≥0 guarantees ΔC≤0. If v changes according to the rules in equation (19), then the loss function C will always decrease and not increase [17].


## 2.1.1. Basic Structure of Deep Convolutional Nervous Nets

The use of neuron shared weights greatly reduces the amount of parameters that need to be trained in the neural network. In addition, the shared weight strategy used can be applied to achieve anterior spreading of nerve grids by convolution on the image data. Connected after the deconvolution stage is the pooling layer, which is used for nonlinear downsampling [7, 8]. Between the convolution and pooling layers is the activation function, which mimics the neuron’s response to input. After the convolution and pooling layer, the characteristic output of deconvolution evaluation and then the full link layer is connected.(1) Full Link Layer. Generally speaking, the channel refers to the color channel of the picture, and the feature map is the output result of the convolution filter. But in fact, the two are essentially the same; both are data representing the distribution of a certain feature on the previous input. In the fully linked stage of the nervous nets, when understanding the output of a layer, the weight vector used by each neuron of the output is not the same. The weighted input and output calculation formulas of the l layer are shown:
(1)zjl=∑k=03wjklakl−1+bjl,j=0,1,(2)ajl=σzjl,j=0,1,where zjl represents the weighted input of the j neuron in the l stage, ajl represents the output of the j nerve elements in the l stage, bjl shows the bias of the j nerve elements in the l layer, σzjl represents the activation function, and wjkl shows the connection weight from the k nerve elements in the l−1 stage to the j nerve elements in the k stage [9, 10].(2) Convolutional Layer. When performing convolution operations in image processing, the convolution kernel needs to be flipped 180 degrees first. The two-dimensional discrete convolution operation formula in image processing is shown:
(3)Si,j=W∗Ii,j=∑s∑tWi,tIi−s,j−t,where I represents the input image, W means the convolution, and S means the output of the convolution operation. Unlike image processing, the so-called convolution in deep neural networks is actually a correlation function, or cross-correlation function, as shown in
(4)Sa,b=W∗Ia,b=∑s∑tWs,tIa+s,i+t.This formula is almost the same as the convolution operation formula (3), but the core W is not flipped. In neuroscience, the local receptive field means that a neuron can receive the signal output of other upper neurons from different locations and a certain spatial range and integrate them as its own input [11, 12]. The local receptive field of the convolutional neural network is similar to this concept, and the concept of shared weight is introduced. The weighted input and output calculation formulas of the first layer can be expressed by
(5)zx,yl=∑s=02∑t=02ws,tlax+s,y+tl−1+bl,(6)ax,yl=σzx,yl,x,y=0,1,where zx,yl represents the weighted input of the nerve elements in the l layer, ax,yl represents the output of the neurons in the l layer, bl means the shared bias of the nerve elements in the l layer, and σzx,yl means the activation function. It is shown in equations (7), (8), and (9), respectively. At present, the most used activation features in deeply involutional nets is the ReLu feature.
(7)σzx,yl=11+e−zx,yl,(8)σzx,yl=ezx,yl−e−zx,ylezx,yl+e−zx,yl,(9)σzx,yl=max0,zx,yl.Compared with full link, the design of partial receptive field reduces the amount of calculation during forward propagation. Although the design of shared weights does not reduce the forward propagation time, it significantly reduces the need for neural network parameter storage [13]. Weight sharing means that, given an input image, use a convolution kernel to scan the image. The number in the convolution kernel is called the weight. Each position of this image is scanned by the same convolution kernel, so the weights are the same, that is, shared.(3) Pooling Stage. Iked level is typically applied to decrease the number of dimensions of the characteristic masking but to a certain extent retains the features of the original data. The pooling function uses the feature to map the statistical features in a certain location field to replace the feature of the location [14].(4) Loss Function. Generally in neural networks, softmax can be used as the output layer for classification tasks. In fact, it can be considered that softmax outputs the probabilities of several categories. For example, I have a classification task that needs to be divided into three categories. The softmax function can output the probabilities of three categories according to their relative sizes, and the sum of the probabilities is 1. Assuming that the softmax classification is performed on the L layer of the neural network, the L layer has a total of k neurons, and the weighted input and output calculation formulas of the L layer are shown [15, 16]:
(10)zjL=∑kwjkLakL−1+bjL,(11)ajL=ezjL∑kezkL,where zjL represents the weighted input of layer L neurons, ajL represents the output of layer L neurons, and akL−1 represents the output of layer L neurons. From equation (11), the sum of the outputs of all neurons in layer L is equal to 1, that is,
(12)∑j=1kajL=1.The softmax loss function formula is shown:(13)C=−1m∑i=1m∑j=1k1yi=jlogajL.This formula is the loss function ofm samples, and yi represents the label of the i sample. The value range of 1yi=j is only 0 and 1. When the brace equality is established, it is 1, and when it is not, it is 0. It can be seen from equation (13) that the loss function formula of a single sample xi is shown in
(14)Cxi=−logayiL.


## 2.1.2. Gradient Descent

In deep neural networks, the purpose of optimizingC is to find the variable v1,v2,⋯,vn to minimize the value of the loss function. From the knowledge of calculus, when v changes by a small increment, the increment of the loss function C can be expressed by its total differential, as shown in
(15)ΔC≈∂C∂v1Δv1+∂C∂v2Δv2+⋯+∂C∂vnΔvn.ΔC can be written as follows:
(16)ΔC≈∇C⋅Δv,(17)∇C=∂C∂v1,∂C∂v2,⋯,∂C∂vnT,(18)Δv=Δv1,Δv2,⋯,ΔvnT.SupposeΔv is as shown in
(19)Δv=−η∇C,(20)ΔC=−η∇C2,where ∇C2≥0 guarantees ΔC≤0. If v changes according to the rules in equation (19), then the loss function C will always decrease and not increase [17].


## 2.2. Network Multimedia Teaching

### 2.2.1. The Definition of Network Multimedia Teaching

Deep learning is to learn the inherent laws and representation levels of sample data, and the information obtained during these learning processes is of great help to the interpretation of data such as text, images, and sounds. Its ultimate goal is to enable machines to have the ability to analyze and learn like humans and to recognize data such as words, images, and sounds. Deep learning is a complex machine learning algorithm that has achieved results in speech and image recognition far exceeding previous related technologies. There is no standardized version of the defense of online multimedia instruction. I define online multimedia teaching as follows: the supposed online multimedia teaching is a new approach to teaching by integrating the most innovative computer network and multimedia technologies completely [18, 19]; it is the application of multimedia technology which is supported by the Internet that will be boring. Through cognitive computing network technique, the boring information in the lecture room is closely mixed into the audio, video, movie, and movements to enable a teaching method based on exchanges and debates.


### 2.2.2. Appearance of Online Multilateral Teaching

With the mainstream development of medial and IT, online multimedia teaching is the third age of distance study. To a degree, network multimedia teaching is a derivative of the innovation of modern day computer network technique and multimedia technology. With the existence and development of computer network techniques and multimedia technologies, bidirectional interactive electric information communications are realized [20–23]. This allows the process of education to be much more overt and flexible. Therefore, with the advent and development of new techniques, network multimedia education emerged.


### 2.2.3. Status Quo of the Development of Multimedia Network Education

Take as a foundation the construction of computer networks in our country and the construction of educational resources as an important factor in the implementation of education. With the construction of campus network infrastructure, the spread and development of the Internet, the constant innovation of multimedia technology, and the large educational population in China, many reasons have attracted many in-house software development companies to turn to the development of multimedia software education networks. In short, the construction of multimedia network education is fully integrated with the support of the local area and all social strata [24, 25].


### 2.2.4. Features of Online Multimedia Teaching

Online multimedia teaching uses modern Internet technology and multimedia technology to break the constraints of traditional time and teaching space. Learners can start their own businesses anytime and anywhere. If they need to use computer equipment through the Internet connection and access the multimedia teaching network, they can choose the courses that suit them and easily obtain the latest and most complete information.


## 2.2.1. The Definition of Network Multimedia Teaching

Deep learning is to learn the inherent laws and representation levels of sample data, and the information obtained during these learning processes is of great help to the interpretation of data such as text, images, and sounds. Its ultimate goal is to enable machines to have the ability to analyze and learn like humans and to recognize data such as words, images, and sounds. Deep learning is a complex machine learning algorithm that has achieved results in speech and image recognition far exceeding previous related technologies. There is no standardized version of the defense of online multimedia instruction. I define online multimedia teaching as follows: the supposed online multimedia teaching is a new approach to teaching by integrating the most innovative computer network and multimedia technologies completely [18, 19]; it is the application of multimedia technology which is supported by the Internet that will be boring. Through cognitive computing network technique, the boring information in the lecture room is closely mixed into the audio, video, movie, and movements to enable a teaching method based on exchanges and debates.


## 2.2.2. Appearance of Online Multilateral Teaching

With the mainstream development of medial and IT, online multimedia teaching is the third age of distance study. To a degree, network multimedia teaching is a derivative of the innovation of modern day computer network technique and multimedia technology. With the existence and development of computer network techniques and multimedia technologies, bidirectional interactive electric information communications are realized [20–23]. This allows the process of education to be much more overt and flexible. Therefore, with the advent and development of new techniques, network multimedia education emerged.


## 2.2.3. Status Quo of the Development of Multimedia Network Education

Take as a foundation the construction of computer networks in our country and the construction of educational resources as an important factor in the implementation of education. With the construction of campus network infrastructure, the spread and development of the Internet, the constant innovation of multimedia technology, and the large educational population in China, many reasons have attracted many in-house software development companies to turn to the development of multimedia software education networks. In short, the construction of multimedia network education is fully integrated with the support of the local area and all social strata [24, 25].


## 2.2.4. Features of Online Multimedia Teaching

Online multimedia teaching uses modern Internet technology and multimedia technology to break the constraints of traditional time and teaching space. Learners can start their own businesses anytime and anywhere. If they need to use computer equipment through the Internet connection and access the multimedia teaching network, they can choose the courses that suit them and easily obtain the latest and most complete information.


## 3. Experimental Design of Sports Multimedia Network System

### 3.1. Test Subject

This research is based on the sports multimedia network system we designed and selected ordinary college sports and health video courses. This study selects the first-year physical education students of X University as the research objects and is divided into an experimental group (3 groups) and a control group (3 groups), each with 15 people, a total of 90 people. The experimental group uses the sports multimedia network system designed in this article for daily learning, and the control group uses traditional teaching methods for learning, as shown in Figure1.Figure 1
Sports multimedia network system.At the end of the semester, a questionnaire survey was conducted among all students. A total of 90 people participated in the questionnaire survey, all of which were collected. The questionnaires of these students are the main source of data. The research results will be analyzed and students will be asked before the course starts. The physical fitness test analyzes the degree of differences in the learning of these students and checks the students every half a month during the learning process. Through this experiment, the establishment of a college sports multimedia network system has promoted the diversification of sports learning methods, thereby better improving the quality of teaching and making greater contributions to learning knowledge points, improving sports skills, and strengthening physical fitness [26–29]. Network multimedia teaching is the product of the combination of computer multimedia technology, network communication technology, and modern educational theory. With the rapid development of computers today, network, multimedia, and distance teaching have entered from the unknown world and greatly influenced our teaching mode. This modern teaching method of multimedia teaching will increasingly show its importance. People should sum up experience and lessons in continuous practice, make advantages and eliminate disadvantages, and make network multimedia teaching better serve teaching.


### 3.2. Experimental Method

#### 3.2.1. Experimental Preparation Stage

Distribute the sports network multimedia system we designed to the students participating in the experiment, learn online sports and health video lessons in regular colleges and universities, and then combine sports interview experts, experts, and network technology experts based on research information according to the needs of the subject, and consult a large number of relevant CNKI documents. Prepare with reference to the bibliography and refer to related books and theoretical knowledge, and design a teaching video familiar with the subject content as a questionnaire. At the same time, preliminary tests were conducted on the experimental group and the control group.


#### 3.2.2. Experimental Stage

Let two groups of students study a subject that is the same as a normal university sports and health video course. The experimental group uses the sports multimedia network system designed in this article, and the control group uses traditional teaching methods for learning. The experimental period is one semester. After the experiment, a questionnaire survey was conducted on the experimental group and the control group.


#### 3.2.3. End of Experiment

After testing the overall level of the experimental group and the control group, make sure that the question type is the same as the difficulty of the test paper. Compare the performance differences of the two classes and determine the advantages of the college-oriented multimedia sports network system designed in this article over traditional teaching methods.


### 3.3. Statistical Processing

k is the amount of figure in this study, σ2 is squared error of all investigation conclusions, and P<0.05 shows that the variance found is statistically significant. The formula for calculating reliability is shown.
(21)a=ni−11−∑αi2α2.


## 3.1. Test Subject

This research is based on the sports multimedia network system we designed and selected ordinary college sports and health video courses. This study selects the first-year physical education students of X University as the research objects and is divided into an experimental group (3 groups) and a control group (3 groups), each with 15 people, a total of 90 people. The experimental group uses the sports multimedia network system designed in this article for daily learning, and the control group uses traditional teaching methods for learning, as shown in Figure1.Figure 1
Sports multimedia network system.At the end of the semester, a questionnaire survey was conducted among all students. A total of 90 people participated in the questionnaire survey, all of which were collected. The questionnaires of these students are the main source of data. The research results will be analyzed and students will be asked before the course starts. The physical fitness test analyzes the degree of differences in the learning of these students and checks the students every half a month during the learning process. Through this experiment, the establishment of a college sports multimedia network system has promoted the diversification of sports learning methods, thereby better improving the quality of teaching and making greater contributions to learning knowledge points, improving sports skills, and strengthening physical fitness [26–29]. Network multimedia teaching is the product of the combination of computer multimedia technology, network communication technology, and modern educational theory. With the rapid development of computers today, network, multimedia, and distance teaching have entered from the unknown world and greatly influenced our teaching mode. This modern teaching method of multimedia teaching will increasingly show its importance. People should sum up experience and lessons in continuous practice, make advantages and eliminate disadvantages, and make network multimedia teaching better serve teaching.


## 3.2. Experimental Method

### 3.2.1. Experimental Preparation Stage

Distribute the sports network multimedia system we designed to the students participating in the experiment, learn online sports and health video lessons in regular colleges and universities, and then combine sports interview experts, experts, and network technology experts based on research information according to the needs of the subject, and consult a large number of relevant CNKI documents. Prepare with reference to the bibliography and refer to related books and theoretical knowledge, and design a teaching video familiar with the subject content as a questionnaire. At the same time, preliminary tests were conducted on the experimental group and the control group.


### 3.2.2. Experimental Stage

Let two groups of students study a subject that is the same as a normal university sports and health video course. The experimental group uses the sports multimedia network system designed in this article, and the control group uses traditional teaching methods for learning. The experimental period is one semester. After the experiment, a questionnaire survey was conducted on the experimental group and the control group.


### 3.2.3. End of Experiment

After testing the overall level of the experimental group and the control group, make sure that the question type is the same as the difficulty of the test paper. Compare the performance differences of the two classes and determine the advantages of the college-oriented multimedia sports network system designed in this article over traditional teaching methods.


## 3.2.1. Experimental Preparation Stage

Distribute the sports network multimedia system we designed to the students participating in the experiment, learn online sports and health video lessons in regular colleges and universities, and then combine sports interview experts, experts, and network technology experts based on research information according to the needs of the subject, and consult a large number of relevant CNKI documents. Prepare with reference to the bibliography and refer to related books and theoretical knowledge, and design a teaching video familiar with the subject content as a questionnaire. At the same time, preliminary tests were conducted on the experimental group and the control group.


## 3.2.2. Experimental Stage

Let two groups of students study a subject that is the same as a normal university sports and health video course. The experimental group uses the sports multimedia network system designed in this article, and the control group uses traditional teaching methods for learning. The experimental period is one semester. After the experiment, a questionnaire survey was conducted on the experimental group and the control group.


## 3.2.3. End of Experiment

After testing the overall level of the experimental group and the control group, make sure that the question type is the same as the difficulty of the test paper. Compare the performance differences of the two classes and determine the advantages of the college-oriented multimedia sports network system designed in this article over traditional teaching methods.


## 3.3. Statistical Processing

k is the amount of figure in this study, σ2 is squared error of all investigation conclusions, and P<0.05 shows that the variance found is statistically significant. The formula for calculating reliability is shown.
(21)a=ni−11−∑αi2α2.


## 4. Experimental Sports Multimedia Network System

### 4.1. Evaluation Index System Based on Index Reliability Testing

Theα coefficient above 0.8 means that the effect of index adjustment is very good, and it is acceptable to be above 0.7. Here, we analyze the reliability of each type and select slightly different confidence indicators for each type. The results are shown in Table 1.Table 1
Index data index.
Very clearClearGeneralNot clearChaoticAlphaLearning attitude3.383.794.523.303.190.8473Learning interest3.353.794.673.283.540.8195Learning initiative3.443.664.083.593.050.8751Learn to communicate4.473.654.973.263.060.7638Learning result4.503.894.233.343.600.7198It can be seen from Figure2 that the data obtained from different indicators such as student behavior, learning interest, training background, written communication ability, and training effect have a positive impact on this test (α>0.7). We can see that the scores obtained in the survey questions are somewhat dependent, and the results obtained at this time can be further investigated.Figure 2
Indicator reliability test analysis chart.Investigate the relevance between the various teaching design factors of traditional physical education, and the results are shown in Figure3.Figure 3
Instructional design index.Judging from the data in the above figure, the indicators of the factors covered by the current physical education teaching design mainly focus on factors between 4.5 and 7. On the whole, the evaluation design factor considers more than other factors. It is mainly caused by the current physical education teaching focusing on the teaching effect and ignoring the teaching process. We have made statistics on students’ ratings of the current physical education teaching methods, as shown in Figure4.Figure 4
Students’ evaluation of current physical education teaching.As can be seen from the data in the above figure, most students think that the current physical education teaching methods are not very experienced, and they are not very satisfied with the learning goals and learning content. First, use regression evaluation to understand the basic situation of this multimedia teaching based on deep learning and wireless communication network, as shown in Figure5.Figure 5
Multimedia physical education teaching evaluation.


### 4.2. Based on Student Physical Fitness Test Scores

#### 4.2.1. Analysis Based on the Pretest Results of Six Groups of Students’ Physical Fitness

Through comparative experiments, the changes in the physical fitness test scores of the six groups of students are compared. The purpose is to analyze whether the sports multimedia network structure designed in this paper can help the students’ physical fitness; before the experiment, the six groups of students were in the same time period. Perform the first physical fitness test, and finally analyze the test data, as shown in Table2.Table 2
Physical fitness pretest score data sheet.
ClassVital capacityPull ups100-meter run3000-meter-long run5-meter three-way turn back runExperimental groupA3.303.614.053.093.48B3.463.594.163.273.14C3.723.704.143.043.25Control groupD3.393.654.173.213.00E3.383.454.223.203.15F3.213.444.093.073.19Before learning the general college sports and health video courses, the physical fitness test scores of the control group and the experimental group were similar (P=0.005<0.05). The results of the experiment will not be affected by the physical fitness of individual students, and the preconditions for the beginning of the experiment will be met. The specific situation is shown in Figure 6.Figure 6
Analysis chart of pretest results of physical fitness.


#### 4.2.2. Analysis Based on Each Physical Fitness Test Score of the Control Group

Through comparative experiments, compare the changes in the physical fitness test scores of the control group. The purpose is to analyze whether the sports multimedia network structure contributes to the improvement of the students’ physical fitness; the data analysis of the physical fitness test scores of the control group shows the results as shown in Table3.Table 3
Data sheet of each physical fitness test score of the control group.
Testing frequencyVital capacityPull ups100-meter run3000-meter-long run5-meter three-way turn back runFirst test3.053.093.583.432.95Second test3.503.062.913.083.11Third test3.392.702.953.113.59Fourth test3.562.982.973.073.56Fifth test3.973.263.743.623.49Sixth test3.873.883.814.063.97It can be seen from Figure7 that after the students in the control group have studied for one semester, the physical fitness test level of the control group has been partially improved (the physical fitness test level in the control group has increased from 3.05 points to 3.87 points), which shows that the use of traditional teaching methods can also improve the quality of students.Figure 7
Analysis chart of each physical fitness test score of the control group.


#### 4.2.3. Analysis Based on each Physical Fitness Test Score of the Experimental Group

Through comparative experiments, compare the changes in the physical fitness test scores of the experimental group. The purpose is to analyze whether the sports multimedia network structure contributes to the improvement of the students’ physical fitness; data analysis of each physical fitness test score of the experimental group shows the results as shown in Table4.Table 4
Data sheet for each physical fitness test score of the experimental group.
Testing frequencyVital capacityPull ups100-meter run3000-meter-long run5-meter three-way turn back runFirst test3.243.592.763.223.16Second test3.993.933.603.373.49Third test4.433.813.723.854.36Fourth test4.524.274.764.724.50Fifth test4.705.374.824.984.92Sixth test5.585.565.255.345.32From Figure8, we can see that after the experimental group students have systematically studied the course throughout the semester, the experimental group’s physical fitness test scores have been greatly improved (the experimental group’s physical fitness test scores have increased from 3.24 to 5.58 points), which demonstrates the effectiveness of college sports multimedia network structure teaching based on deep learning network environment, and at the same time comprehensively cultivate students’ sense of practice and provide students with a certain amount of space.Figure 8
Analysis chart of each physical fitness test score of the experimental group.


#### 4.2.4. Analysis Based on the Posttest Results of the Physical Fitness of the Six Groups of Students

Through comparative experiments, the changes in the physical fitness test scores of the six groups of students are compared, and the purpose is to analyze whether the sports multimedia network structure designed in this paper can help students improve their professional class performance; after the experiment, the six groups of students at the same time perform the last physical fitness test, and finally, the test data were analyzed, as shown in Table5.Table 5
Physical fitness posttest result data sheet.
ClassVital capacityPull ups100-meter run3000-meter-long run5-meter three-way turn back runExperimental groupA5.165.895.555.995.73B5.515.535.365.655.76C5.585.565.255.345.32Control groupD4.203.714.334.423.73E4.314.494.144.113.96F3.774.383.834.214.34After the six groups of students took the course systematically throughout the semester, the physical fitness test scores of the control group have been partially improved (the physical fitness test scores of the control group increased from 3.05 points to 3.87 points), and the physical fitness test scores of the experimental group students have been greatly improved (the experimental group’s physical fitness test scores increased from 3.24 points to 5.58 points), which shows that the use of sports multimedia network structure for learning and traditional teaching methods can enable students to gain physical fitness. However, the effect of using the sports multimedia network structure for learning is better than traditional teaching methods. This proves that the university sports multimedia network structure based on the deep learning network environment has a broader development prospect. The specific situation is shown in Figure9.Figure 9
Analysis chart of posttest results of physical fitness.


### 4.3. Testing of Sports Multimedia Network Structure

#### 4.3.1. Structure Function Test

Check system performance first with the basic functions of recording, access, performance information, performance display, technical fields, technical analysis, debugging, etc. During the monitoring effect test process, you can see from the table that the foundation was designed and tested successfully after analyzing the required functions.


#### 4.3.2. Examine the Work Situation

Here, we test the performance status of the system and test the maximum number of users connected simultaneously to the background. We selected 50, 100, 250, and 500, respectively, for the test. According to the above results, Figure10 can be obtained.Figure 10
Running state test chart.You can see from Figure10 that when 100 people do the connection work of the program at the same time, the program starts to feel a little weird. When 500 people do the established connection service at the same time, the system starts to make a few mistakes, but at least 50 people do the same time; the Taiwanese connection service will not cause more delays and more errors.


## 4.1. Evaluation Index System Based on Index Reliability Testing

Theα coefficient above 0.8 means that the effect of index adjustment is very good, and it is acceptable to be above 0.7. Here, we analyze the reliability of each type and select slightly different confidence indicators for each type. The results are shown in Table 1.Table 1
Index data index.
Very clearClearGeneralNot clearChaoticAlphaLearning attitude3.383.794.523.303.190.8473Learning interest3.353.794.673.283.540.8195Learning initiative3.443.664.083.593.050.8751Learn to communicate4.473.654.973.263.060.7638Learning result4.503.894.233.343.600.7198It can be seen from Figure2 that the data obtained from different indicators such as student behavior, learning interest, training background, written communication ability, and training effect have a positive impact on this test (α>0.7). We can see that the scores obtained in the survey questions are somewhat dependent, and the results obtained at this time can be further investigated.Figure 2
Indicator reliability test analysis chart.Investigate the relevance between the various teaching design factors of traditional physical education, and the results are shown in Figure3.Figure 3
Instructional design index.Judging from the data in the above figure, the indicators of the factors covered by the current physical education teaching design mainly focus on factors between 4.5 and 7. On the whole, the evaluation design factor considers more than other factors. It is mainly caused by the current physical education teaching focusing on the teaching effect and ignoring the teaching process. We have made statistics on students’ ratings of the current physical education teaching methods, as shown in Figure4.Figure 4
Students’ evaluation of current physical education teaching.As can be seen from the data in the above figure, most students think that the current physical education teaching methods are not very experienced, and they are not very satisfied with the learning goals and learning content. First, use regression evaluation to understand the basic situation of this multimedia teaching based on deep learning and wireless communication network, as shown in Figure5.Figure 5
Multimedia physical education teaching evaluation.


## 4.2. Based on Student Physical Fitness Test Scores

### 4.2.1. Analysis Based on the Pretest Results of Six Groups of Students’ Physical Fitness

Through comparative experiments, the changes in the physical fitness test scores of the six groups of students are compared. The purpose is to analyze whether the sports multimedia network structure designed in this paper can help the students’ physical fitness; before the experiment, the six groups of students were in the same time period. Perform the first physical fitness test, and finally analyze the test data, as shown in Table2.Table 2
Physical fitness pretest score data sheet.
ClassVital capacityPull ups100-meter run3000-meter-long run5-meter three-way turn back runExperimental groupA3.303.614.053.093.48B3.463.594.163.273.14C3.723.704.143.043.25Control groupD3.393.654.173.213.00E3.383.454.223.203.15F3.213.444.093.073.19Before learning the general college sports and health video courses, the physical fitness test scores of the control group and the experimental group were similar (P=0.005<0.05). The results of the experiment will not be affected by the physical fitness of individual students, and the preconditions for the beginning of the experiment will be met. The specific situation is shown in Figure 6.Figure 6
Analysis chart of pretest results of physical fitness.


### 4.2.2. Analysis Based on Each Physical Fitness Test Score of the Control Group

Through comparative experiments, compare the changes in the physical fitness test scores of the control group. The purpose is to analyze whether the sports multimedia network structure contributes to the improvement of the students’ physical fitness; the data analysis of the physical fitness test scores of the control group shows the results as shown in Table3.Table 3
Data sheet of each physical fitness test score of the control group.
Testing frequencyVital capacityPull ups100-meter run3000-meter-long run5-meter three-way turn back runFirst test3.053.093.583.432.95Second test3.503.062.913.083.11Third test3.392.702.953.113.59Fourth test3.562.982.973.073.56Fifth test3.973.263.743.623.49Sixth test3.873.883.814.063.97It can be seen from Figure7 that after the students in the control group have studied for one semester, the physical fitness test level of the control group has been partially improved (the physical fitness test level in the control group has increased from 3.05 points to 3.87 points), which shows that the use of traditional teaching methods can also improve the quality of students.Figure 7
Analysis chart of each physical fitness test score of the control group.


### 4.2.3. Analysis Based on each Physical Fitness Test Score of the Experimental Group

Through comparative experiments, compare the changes in the physical fitness test scores of the experimental group. The purpose is to analyze whether the sports multimedia network structure contributes to the improvement of the students’ physical fitness; data analysis of each physical fitness test score of the experimental group shows the results as shown in Table4.Table 4
Data sheet for each physical fitness test score of the experimental group.
Testing frequencyVital capacityPull ups100-meter run3000-meter-long run5-meter three-way turn back runFirst test3.243.592.763.223.16Second test3.993.933.603.373.49Third test4.433.813.723.854.36Fourth test4.524.274.764.724.50Fifth test4.705.374.824.984.92Sixth test5.585.565.255.345.32From Figure8, we can see that after the experimental group students have systematically studied the course throughout the semester, the experimental group’s physical fitness test scores have been greatly improved (the experimental group’s physical fitness test scores have increased from 3.24 to 5.58 points), which demonstrates the effectiveness of college sports multimedia network structure teaching based on deep learning network environment, and at the same time comprehensively cultivate students’ sense of practice and provide students with a certain amount of space.Figure 8
Analysis chart of each physical fitness test score of the experimental group.


### 4.2.4. Analysis Based on the Posttest Results of the Physical Fitness of the Six Groups of Students

Through comparative experiments, the changes in the physical fitness test scores of the six groups of students are compared, and the purpose is to analyze whether the sports multimedia network structure designed in this paper can help students improve their professional class performance; after the experiment, the six groups of students at the same time perform the last physical fitness test, and finally, the test data were analyzed, as shown in Table5.Table 5
Physical fitness posttest result data sheet.
ClassVital capacityPull ups100-meter run3000-meter-long run5-meter three-way turn back runExperimental groupA5.165.895.555.995.73B5.515.535.365.655.76C5.585.565.255.345.32Control groupD4.203.714.334.423.73E4.314.494.144.113.96F3.774.383.834.214.34After the six groups of students took the course systematically throughout the semester, the physical fitness test scores of the control group have been partially improved (the physical fitness test scores of the control group increased from 3.05 points to 3.87 points), and the physical fitness test scores of the experimental group students have been greatly improved (the experimental group’s physical fitness test scores increased from 3.24 points to 5.58 points), which shows that the use of sports multimedia network structure for learning and traditional teaching methods can enable students to gain physical fitness. However, the effect of using the sports multimedia network structure for learning is better than traditional teaching methods. This proves that the university sports multimedia network structure based on the deep learning network environment has a broader development prospect. The specific situation is shown in Figure9.Figure 9
Analysis chart of posttest results of physical fitness.


## 4.2.1. Analysis Based on the Pretest Results of Six Groups of Students’ Physical Fitness

Through comparative experiments, the changes in the physical fitness test scores of the six groups of students are compared. The purpose is to analyze whether the sports multimedia network structure designed in this paper can help the students’ physical fitness; before the experiment, the six groups of students were in the same time period. Perform the first physical fitness test, and finally analyze the test data, as shown in Table2.Table 2
Physical fitness pretest score data sheet.
ClassVital capacityPull ups100-meter run3000-meter-long run5-meter three-way turn back runExperimental groupA3.303.614.053.093.48B3.463.594.163.273.14C3.723.704.143.043.25Control groupD3.393.654.173.213.00E3.383.454.223.203.15F3.213.444.093.073.19Before learning the general college sports and health video courses, the physical fitness test scores of the control group and the experimental group were similar (P=0.005<0.05). The results of the experiment will not be affected by the physical fitness of individual students, and the preconditions for the beginning of the experiment will be met. The specific situation is shown in Figure 6.Figure 6
Analysis chart of pretest results of physical fitness.


## 4.2.2. Analysis Based on Each Physical Fitness Test Score of the Control Group

Through comparative experiments, compare the changes in the physical fitness test scores of the control group. The purpose is to analyze whether the sports multimedia network structure contributes to the improvement of the students’ physical fitness; the data analysis of the physical fitness test scores of the control group shows the results as shown in Table3.Table 3
Data sheet of each physical fitness test score of the control group.
Testing frequencyVital capacityPull ups100-meter run3000-meter-long run5-meter three-way turn back runFirst test3.053.093.583.432.95Second test3.503.062.913.083.11Third test3.392.702.953.113.59Fourth test3.562.982.973.073.56Fifth test3.973.263.743.623.49Sixth test3.873.883.814.063.97It can be seen from Figure7 that after the students in the control group have studied for one semester, the physical fitness test level of the control group has been partially improved (the physical fitness test level in the control group has increased from 3.05 points to 3.87 points), which shows that the use of traditional teaching methods can also improve the quality of students.Figure 7
Analysis chart of each physical fitness test score of the control group.


## 4.2.3. Analysis Based on each Physical Fitness Test Score of the Experimental Group

Through comparative experiments, compare the changes in the physical fitness test scores of the experimental group. The purpose is to analyze whether the sports multimedia network structure contributes to the improvement of the students’ physical fitness; data analysis of each physical fitness test score of the experimental group shows the results as shown in Table4.Table 4
Data sheet for each physical fitness test score of the experimental group.
Testing frequencyVital capacityPull ups100-meter run3000-meter-long run5-meter three-way turn back runFirst test3.243.592.763.223.16Second test3.993.933.603.373.49Third test4.433.813.723.854.36Fourth test4.524.274.764.724.50Fifth test4.705.374.824.984.92Sixth test5.585.565.255.345.32From Figure8, we can see that after the experimental group students have systematically studied the course throughout the semester, the experimental group’s physical fitness test scores have been greatly improved (the experimental group’s physical fitness test scores have increased from 3.24 to 5.58 points), which demonstrates the effectiveness of college sports multimedia network structure teaching based on deep learning network environment, and at the same time comprehensively cultivate students’ sense of practice and provide students with a certain amount of space.Figure 8
Analysis chart of each physical fitness test score of the experimental group.


## 4.2.4. Analysis Based on the Posttest Results of the Physical Fitness of the Six Groups of Students

Through comparative experiments, the changes in the physical fitness test scores of the six groups of students are compared, and the purpose is to analyze whether the sports multimedia network structure designed in this paper can help students improve their professional class performance; after the experiment, the six groups of students at the same time perform the last physical fitness test, and finally, the test data were analyzed, as shown in Table5.Table 5
Physical fitness posttest result data sheet.
ClassVital capacityPull ups100-meter run3000-meter-long run5-meter three-way turn back runExperimental groupA5.165.895.555.995.73B5.515.535.365.655.76C5.585.565.255.345.32Control groupD4.203.714.334.423.73E4.314.494.144.113.96F3.774.383.834.214.34After the six groups of students took the course systematically throughout the semester, the physical fitness test scores of the control group have been partially improved (the physical fitness test scores of the control group increased from 3.05 points to 3.87 points), and the physical fitness test scores of the experimental group students have been greatly improved (the experimental group’s physical fitness test scores increased from 3.24 points to 5.58 points), which shows that the use of sports multimedia network structure for learning and traditional teaching methods can enable students to gain physical fitness. However, the effect of using the sports multimedia network structure for learning is better than traditional teaching methods. This proves that the university sports multimedia network structure based on the deep learning network environment has a broader development prospect. The specific situation is shown in Figure9.Figure 9
Analysis chart of posttest results of physical fitness.


## 4.3. Testing of Sports Multimedia Network Structure

### 4.3.1. Structure Function Test

Check system performance first with the basic functions of recording, access, performance information, performance display, technical fields, technical analysis, debugging, etc. During the monitoring effect test process, you can see from the table that the foundation was designed and tested successfully after analyzing the required functions.


### 4.3.2. Examine the Work Situation

Here, we test the performance status of the system and test the maximum number of users connected simultaneously to the background. We selected 50, 100, 250, and 500, respectively, for the test. According to the above results, Figure10 can be obtained.Figure 10
Running state test chart.You can see from Figure10 that when 100 people do the connection work of the program at the same time, the program starts to feel a little weird. When 500 people do the established connection service at the same time, the system starts to make a few mistakes, but at least 50 people do the same time; the Taiwanese connection service will not cause more delays and more errors.


## 4.3.1. Structure Function Test

Check system performance first with the basic functions of recording, access, performance information, performance display, technical fields, technical analysis, debugging, etc. During the monitoring effect test process, you can see from the table that the foundation was designed and tested successfully after analyzing the required functions.


## 4.3.2. Examine the Work Situation

Here, we test the performance status of the system and test the maximum number of users connected simultaneously to the background. We selected 50, 100, 250, and 500, respectively, for the test. According to the above results, Figure10 can be obtained.Figure 10
Running state test chart.You can see from Figure10 that when 100 people do the connection work of the program at the same time, the program starts to feel a little weird. When 500 people do the established connection service at the same time, the system starts to make a few mistakes, but at least 50 people do the same time; the Taiwanese connection service will not cause more delays and more errors.


## 5. Conclusions

This paper expounds the wireless communication and deep learning network environment and analyzes the compatibility of traditional high school education network. Based on the feedback process and programming resources, we developed an automatic source training algorithm, which provided a scientific basis for the implementation of deep learning-based network systems in several sports colleges. In the process of perfecting online training, a new online training mode guided by teacher self-training and student self-training is developed. This article first analyzes the current state of research and development of online education and multimedia technology. The general design of this program is based on the real needs of online sports education in our country and the design of the main functional component of the program has been established. The design of the multisport network in this piece best reflects the role of the teacher and the student’s ability and enhances the cohesive outcomes both in and out of the classroom. It helps a lot in developing student training and innovation capacity. It can promote the maintenance and effective use of physical education resources and has excellent results beyond the model of teaching and learning resources.



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*Source: 3267639-2022-04-23.xml*