# Construction and 3D Simulation of Virtual Animation Instant Network Communication System Based on Convolution Neural Networks

Authors: Qiang Wu
Journal: Computational Intelligence and Neuroscience
 (2021)

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## Abstract

In recent years, great progress has been made in 3D simulation modeling of instant network communication system, such as the application of virtual reality technology and 3D virtual animation online modeling technology. Facing the increasing demand of different industries, how to build an instant network communication system for 3D virtual animation has become a research hotspot. On this basis, the construction method of fast instant network communication system based on convolutional neural network and fusion morphological 3D simulation model is studied. This paper analyzes the research status of instant network communication system. The experiment optimizes and improves the shortcomings of the current research hotspot of virtual animation instant network communication system and takes the morphological 3D simulation model fusion as the core for in-depth optimization. Finally, the experimental results show that the fusion morphological 3D simulation model can reconstruct the standard 3D virtual animation model according to different needs and can quickly model the optimization strategy according to the local differences of different animations. The response accuracy of the network communication system reaches 97.7%.


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## Body

## 1. Introduction

There are many problems in 3D virtual animation, such as low authenticity and slow modeling speed [1]. How to use the existing intelligent algorithm and 3D laser scanning technology to realize the rapid modeling of 3D virtual animation of animation and then realize the deeper realistic animation modeling has become a major research hotspot [2]. In addition, when modeling 3D virtual animation models, because different animations have different local features, these differences will affect the realism and modeling efficiency of animation modeling [3]. And, because most 3D virtual animation models are in the process of modeling, there are many reasons for poor authenticity, including different levels of local modeling differences, lighting modeling differences, and character modeling differences. As an external interference factor, the input of 3D parameters affects the speed and effect reliability of the instant network communication system, so as to affect the real instant network communication system results [4]. Therefore, for 3D virtual animation models with different requirements, it is very important to identify and correct 3D simulation and enhance the realism before modeling [5].The contributions of this paper are as follows. (1) We combine the deep convolutional neural network algorithm to propose a 3D virtual animation-oriented instant network communication system and a three-dimensional fast modeling model. (2) We study the method of 3D reconstruction of virtual animation, according to the local feature differences, feature tracking, and modeling speed between different 3D virtual animation models, and a 3D virtual animation-oriented 3D based on fusion form is constructed. (3) We build an instant network communication system for 3D virtual animation.The rest research is as follows. Section2 introduces and summarizes the related works. Section 3 devotes to research of the establishment of the basic 3D virtual animation instant network communication system and 3D fast simulation model. In Section 4, construction and 3D simulation of the 3D virtual animation instant network communication system based on convolution neural networks are given. In Section 5, result analysis and discussion are provided. In Section 6, we make a summary of the full text.


## 2. Related Work

In recent years, David et al. have studied the application of 3D virtual animation fast modeling algorithm in the network communication system [6]. Fengying et al. have established a 3D virtual animation capture model by using 3D laser scanner and confirmed the feasibility of 3D virtual prototyping technology to study animation modeling, which provides a good way for 3D virtual animation design [7]. Haenssle et al. have studied the method of instant network communication system and realistic optimization strategy, selected neural network algorithm, and verified the effectiveness of the method by modeling the local of two groups of different people [8]. Through the combination of instant network communication system and feature capture, the coupling relationship is studied. Finally, through the design experiment, the effect of this method in 3D modeling is proved [9]. Kadiri et al. studied the impact of different dynamic performance on the speed of the instant network communication system through three-dimensional simulation technology. By selecting the application of greedy algorithm in the process of the instant network communication system and according to a large number of animation samples, the error of the function was repaired and verified. Finally, the effectiveness and feasibility of the animation modeling method are verified [10]. Kanno et al. found that the current 3D virtual animation network communication system can provide solutions for the problems of the instant network communication system in various environments [11]. Khan et al. used 3D simulation software to establish a fast reconstruction model of 3D virtual animation, and then through simulation analysis and modeling of different animation data and with reference to the relevant standard evaluation and animation modeling strategy, they provided a new research idea for the research of fast modeling of 3D virtual animation [12]. Kumari and Singh analyzed and verified the factors influencing the speed of the instant network communication system through the finite element analysis of 3D animation structure and symmetry calculation [13]. Lee et al. have conducted various research studies on the common instant network communication system environment and introduced the instant network communication system model and particle swarm optimization algorithm with higher accuracy and better reconstruction effect [14]. Liao et al. proposed an algorithm for the instant network communication system based on Bernoulli matrix. The results show that the algorithm can reduce the local feature dislocation recognition problem in instant network communication system, but it also reduces the recognition accuracy [15]. In order to improve the accuracy, Ma et al. put forward the optimization model of the instant network communication system based on ant colony algorithm and particle swarm algorithm. Through the design experiment, the effectiveness of the model was verified, and through the fast modeling algorithm of 3D virtual animation simulating animation, the accurate recognition of local features of animation was realized and the quality of the instant network communication system for film and television is improved [16]. Modern research mainly focuses on the research and model construction of the instant network communication system evaluation based on the fusion morphological 3D simulation model. [17]. Therefore, it is of great practical significance to study the algorithm of the 3D virtual animation instant network communication system based on convolution neural networks’ algorithm and fusion of the morphological 3D simulation model [18]. It can be found that most of the research results of scholars in this field are innovations and technological breakthroughs in the three-dimensional simulation of the system, and few have improved the effect of realizing the high-efficiency transmission of three-dimensional simulation data from the instant network communication system.


## 3. The Establishment of the Basic 3D Virtual Animation Instant Network Communication System and 3D Fast Simulation Model

According to the current common types of virtual animation and the standard range of animation size, the size of the 3D animation model to be established is obtained [19]. In this paper, the standard animation database, edgebod, which is commonly used in animation modeling, is used to model and analyze according to the characteristics and differences of animation models. The specific parameters of the 3D virtual animation model are shown in Table 1.Table 1
3D virtual animation model parameters.
3D virtual animation modelParametersFeature240The complexity165Correlation68Coupling application degree68Trueness86The fast modeling algorithm of 3D virtual animation and the intelligent processing model of local data information based on the existing animation proposed in this paper adopt the multilayer fusion morphological 3D simulation model and structure. It is assumed that the first input value of the target, the hidden input value of the security feature, the output layer data of the security judgment, and the number of nodes needed for the fast modeling algorithm of 3D virtual animation are known. The data description of the 3D local model adopts the standard evaluation method.The instant network communication system of 3D virtual animation and the fast modeling method of 3D simulation are studied. The second part introduces the research status of instant network communication system technology and analyzes the possibility of innovation according to the existing research results. The fast modeling algorithm of 3D virtual animation based on the fusion morphological 3D simulation model is constructed, the optimization method of instant network communication system based on Gaussian random distribution is proposed, and the quality evaluation system of the instant network communication system is constructed. The experiment of the instant network communication system and three-dimensional simulation algorithm is designed. The experimental results are analyzed and the conclusion is drawn. Therefore, the innovation of this study is to propose a fast modeling method of 3D virtual animation based on morphological 3D simulation model fusion. Based on the rough recognition of local features of animation, this model makes full use of the perceptual information of animation to realize the dynamic and fast modeling of virtual animation. The similarity and consistency between the comparison bar (realistic effect of the target animation instant network communication system) and the reference bar (fast modeling effect of known high-quality 3D virtual animation) is quantitatively described. It can make full use of the local differences of each virtual animation to realize the rapid modeling of information transmission of the instant messaging system in 3D simulation.


## 4. Construction and 3D Simulation of the 3D Virtual Animation Instant Network Communication System Based on Convolution Neural Networks

### 4.1. 3D Virtual Animation Instant Network Communication System Based on Convolution Neural Networks

The dynamic local tracking process of 3D virtual animation based on convolution neural networks is mainly divided into the basic model and the structural model. The basic model includes the sequential model and the parallel model, and the structural model includes the cyclic model [20]. Combined with the convolution neural networks’ algorithm and the fusion of the morphological 3D simulation model and the simulation model, the animation local modeling process is simulated and analyzed. The general algorithm principle of convolution neural networks is shown in Figure 1.Figure 1
The principle of deep convolutional neural network algorithm.According to the characteristics of collaborative simulation process, the local feature reconstruction process model can be divided into the basic model and the control model [21]. According to the above conclusions, it can be found that, in the process of 3D virtual animation reconstruction and information transmission, the control model is a process model with conditional connection mode, and the data analysis process is shown in Figure 2.Figure 2
Data analysis process of convolutional neural network.The design processp1 and p3 are all serial structures, the design process p3 and p1 are conditional serial structures, and p is conditional probability. When it is less than or equal to p2, after the design process p3 is executed, the whole design process ends without iteration. In view of this situation, the 3D virtual animation reconstruction model of the design structure in the previous section is extended, and the 3D animation modeling is carried out through the multivalue fusion morphological 3D simulation model [22]. The multidimensional matrix is used to describe the calculation cycle, sequential coupling degree, and local facial coupling degree in the process of 3D virtual animation rapid modeling, which is called multivalued 3D virtual animation fusion morphological simulation model [23]. Let the local matrix be(1)Ai,j=a1,1⋮1i,1,Ai,j=T+Do,i≠j,T+1T+Dss,i=j,where T represents the calculation period of each animation modeling process node, the sequential coupling degree between Do process nodes, and the local facial coupling degree set between Dss process nodes. According to the definition, the calculation period of each local 3D simulation process node is t, and the number of motion process nodes is n. The calculation period of the whole local 3D simulation process is represented by the calculation period matrix Ad; then, Ad is a one-dimensional matrix, and its matrix value is(2)Ad=titi+ti−1,i=1,2,3,…,n.The corrected value is(3)Ad′=2ti3ti+2ti−1,i=1,2,3,…,n.The order coupling degree of any two 3D local simulation process nodes ist, and the number of 3D simulation process nodes is n. The order coupling degree of the whole local 3D simulation process is expressed by the order coupling degree matrix Ab; then, Ab is a two-dimensional matrix, and the matrix value is(4)Ab′=3ti−2ti3ti+2ti−1,i=1,2,3,…,n.The modified matrix value is(5)Ab″=6ti+ti−1−ti+13ti+2ti−1,i=1,2,3,…,n.According to the value ofti, we can judge the order dependence between two process nodes. The judgment method is as follows: when i≠j,ai,j=1,ai,j=0, it means that the design process represented by row i and column j has order dependence. The order is as follows. If i<j, then the design process j is before the design process i. At this time, the matrix value is(6)Bb=9ti+ti−1−2ti+15ti+2ti−1,i=1,2,3,…,n.Ifi>j, then the design process i is before the design process j and the matrix value is(7)Bb′=11ti+3ti−1−2ti+17ti+2ti−1+2ti+1,i=1,2,3,…,n.Wheni≠j,bi,j=1,bi,j=1, it means that the design process represented by row i and column j has a sequential coupling relationship, and the value of the judgment matrix is(8)Bb″=13ti+5ti−1−3ti+15ti+2ti−1+ti+1,i=1,2,3,…,n.The design processi and j represent different sequences; the calculation cycle of the design process is different, so the sequence is determined according to the scheme of the shortest 3D virtual animation fast modeling time.The time of different collaborative local 3D simulation processes varies greatly; for example, a simple calculation process takes a few seconds. If the calculation period of local modeling process is a long period, the calculation period is divided by 5 seconds, and the value of the corresponding period matrixCb is(9)Cb=16ti+7ti−1−3ti+12ti+2ti−1+3ti+1,i=1,2,3,…,n.After coupling analysis and optimization, the value of matrixCb′ can be expressed as(10)Cb′=ti2+7ti−1−3ti+137ti2+5ti−1+3ti+13,i=1,2,3,…,n.In the process of matrix value optimization, if the calculation period of local simulation process belongs to the medium period, the calculation period is divided by 5 minutes and seconds at the same time. If the calculation period of the local 3D simulation process is a short period, the calculation period is divided by 5 ms at the same time. After normalization, the calculation periodT of the process is a dimensionless value. For simple multivalue fusion morphological feature model, diagonal is the calculation period; the first of other matrix values is sequential coupling degree, others are resource coupling degree, and zero value means uncoupled [24].


### 4.2. Simulation Process of Instant Network Communication System and Fast Modeling Algorithm for 3D Virtual Animation

In this paper, we first simulate the common animation local features. According to the fusion morphological 3D simulation model and 3D virtual animation reconstruction algorithm proposed above, we simulate the distribution of common animation local features. The data processing flow of 3D simulation animation is shown in Figure3.Figure 3
Data processing flow of 3D simulation animation.In the process of 3D reconstruction of animation, different parts of the animation show different 3D simulation changes, and because of the local differences between each animation itself, there are also differences in the 3D simulation process between different parts and the simulation process of local features. Therefore, this study first collected a large number of animation common local data as training samples and then combined with access analysis software for local difference model reconstruction, to achieve the 3D reconstruction of different animation local posture, in order to help improve the effect and accuracy of the animation instant network communication system. In the process of local difference analysis, the value of difference degree can be calculated according to the feature factordi, so as to achieve quantitative representation [25]. In the process of 3D simulation reconstruction of virtual animation with single feature, the difference degree di can be expressed as(11)e=∑i=1n3di3+di2+7di−1di2.Considering the difference of different types of virtual animation in the feature value, the disturbance correction is made. At this time, the difference can be expressed as(12)e′=n2∑i=1n3di3+di2+7di−1∑i=1ndi2.On the contrary, in order to better analyze the local stability and standardization of animation, we also established a basic instant network communication system database in the experiment process to realize continuous updating and correction. The data processing flow is shown in Figure4.Figure 4
Data processing flow of instant network communication system database.On the contrary, because this research is based on the convolution neural networks algorithm and is oriented to 3D virtual animation, so, in the process of simulation experiment, we need to add constraints according to the feature factordi, that is, the first step is to decompose the known local 3D information of common animation and express and store it in the form of matrix. This constraint process can be expressed as(13)ε=∑i=1ndi3+3di2+2di−1di3.In order to improve the reliability of constraints, the constraint process can be expressed as follows:(14)ε′=∑i=1ndi3+3di2+2di−1/di3di2+4di−1.In this way, the local features of different animations can be dynamically changed and recognized. In this process, different fusion forms of 3D simulation models are introduced, and Gauss random matrix and Bernoulli matrix are used to store vectors to realize the recognition and storage of different types of 3D virtual animation communication information and gait matching. The simulation results of data are shown in Figure5.Figure 5
Analysis results of different 3D animation simulation data.As can be seen from Figure5, in different data, with the increase of convolution times, the coincidence degree is different. The matching degree of the first group of data is getting lower and lower, while the matching degree of the other two groups of data is getting higher and higher. The simulation process can also be characterized by the influence of characteristic factors on the correlation, that is, the quantitative evaluation of the correlation function:(15)fd=∑i=1n3di+12+2di−12ndi2.Through the analysis of correlation function, the feature vectors of different gait vectors are solved, and the point product multiplication is carried out to realize the classification and differentiation of different gait vectors. Then, local feature detection and multidimensional reconstruction of animation are carried out, including video detection, image detection, and local feature factor-tracking detection. The simulation results of three groups of data are shown in Figure6.Figure 6
Instant network communication data simulation results.From Figure6, it can be seen that different data have slight fluctuation trend. This is because, in the rapid reconstruction of 3D virtual animation dynamic instant network communication system, in the simulation process, different constraint thresholds and reconstruction models are set according to the different characteristics of 3D virtual animation, and then, these data are input into the 3D virtual animation rapid modeling system to convert the known information into data vector information and standard vector for calculation. The similarity of the fast modeling algorithms for 3D virtual animation of these vectors is analyzed. Finally, the classification analysis and reality enhancement are carried out, and the expected standard is achieved.


## 4.1. 3D Virtual Animation Instant Network Communication System Based on Convolution Neural Networks

The dynamic local tracking process of 3D virtual animation based on convolution neural networks is mainly divided into the basic model and the structural model. The basic model includes the sequential model and the parallel model, and the structural model includes the cyclic model [20]. Combined with the convolution neural networks’ algorithm and the fusion of the morphological 3D simulation model and the simulation model, the animation local modeling process is simulated and analyzed. The general algorithm principle of convolution neural networks is shown in Figure 1.Figure 1
The principle of deep convolutional neural network algorithm.According to the characteristics of collaborative simulation process, the local feature reconstruction process model can be divided into the basic model and the control model [21]. According to the above conclusions, it can be found that, in the process of 3D virtual animation reconstruction and information transmission, the control model is a process model with conditional connection mode, and the data analysis process is shown in Figure 2.Figure 2
Data analysis process of convolutional neural network.The design processp1 and p3 are all serial structures, the design process p3 and p1 are conditional serial structures, and p is conditional probability. When it is less than or equal to p2, after the design process p3 is executed, the whole design process ends without iteration. In view of this situation, the 3D virtual animation reconstruction model of the design structure in the previous section is extended, and the 3D animation modeling is carried out through the multivalue fusion morphological 3D simulation model [22]. The multidimensional matrix is used to describe the calculation cycle, sequential coupling degree, and local facial coupling degree in the process of 3D virtual animation rapid modeling, which is called multivalued 3D virtual animation fusion morphological simulation model [23]. Let the local matrix be(1)Ai,j=a1,1⋮1i,1,Ai,j=T+Do,i≠j,T+1T+Dss,i=j,where T represents the calculation period of each animation modeling process node, the sequential coupling degree between Do process nodes, and the local facial coupling degree set between Dss process nodes. According to the definition, the calculation period of each local 3D simulation process node is t, and the number of motion process nodes is n. The calculation period of the whole local 3D simulation process is represented by the calculation period matrix Ad; then, Ad is a one-dimensional matrix, and its matrix value is(2)Ad=titi+ti−1,i=1,2,3,…,n.The corrected value is(3)Ad′=2ti3ti+2ti−1,i=1,2,3,…,n.The order coupling degree of any two 3D local simulation process nodes ist, and the number of 3D simulation process nodes is n. The order coupling degree of the whole local 3D simulation process is expressed by the order coupling degree matrix Ab; then, Ab is a two-dimensional matrix, and the matrix value is(4)Ab′=3ti−2ti3ti+2ti−1,i=1,2,3,…,n.The modified matrix value is(5)Ab″=6ti+ti−1−ti+13ti+2ti−1,i=1,2,3,…,n.According to the value ofti, we can judge the order dependence between two process nodes. The judgment method is as follows: when i≠j,ai,j=1,ai,j=0, it means that the design process represented by row i and column j has order dependence. The order is as follows. If i<j, then the design process j is before the design process i. At this time, the matrix value is(6)Bb=9ti+ti−1−2ti+15ti+2ti−1,i=1,2,3,…,n.Ifi>j, then the design process i is before the design process j and the matrix value is(7)Bb′=11ti+3ti−1−2ti+17ti+2ti−1+2ti+1,i=1,2,3,…,n.Wheni≠j,bi,j=1,bi,j=1, it means that the design process represented by row i and column j has a sequential coupling relationship, and the value of the judgment matrix is(8)Bb″=13ti+5ti−1−3ti+15ti+2ti−1+ti+1,i=1,2,3,…,n.The design processi and j represent different sequences; the calculation cycle of the design process is different, so the sequence is determined according to the scheme of the shortest 3D virtual animation fast modeling time.The time of different collaborative local 3D simulation processes varies greatly; for example, a simple calculation process takes a few seconds. If the calculation period of local modeling process is a long period, the calculation period is divided by 5 seconds, and the value of the corresponding period matrixCb is(9)Cb=16ti+7ti−1−3ti+12ti+2ti−1+3ti+1,i=1,2,3,…,n.After coupling analysis and optimization, the value of matrixCb′ can be expressed as(10)Cb′=ti2+7ti−1−3ti+137ti2+5ti−1+3ti+13,i=1,2,3,…,n.In the process of matrix value optimization, if the calculation period of local simulation process belongs to the medium period, the calculation period is divided by 5 minutes and seconds at the same time. If the calculation period of the local 3D simulation process is a short period, the calculation period is divided by 5 ms at the same time. After normalization, the calculation periodT of the process is a dimensionless value. For simple multivalue fusion morphological feature model, diagonal is the calculation period; the first of other matrix values is sequential coupling degree, others are resource coupling degree, and zero value means uncoupled [24].


## 4.2. Simulation Process of Instant Network Communication System and Fast Modeling Algorithm for 3D Virtual Animation

In this paper, we first simulate the common animation local features. According to the fusion morphological 3D simulation model and 3D virtual animation reconstruction algorithm proposed above, we simulate the distribution of common animation local features. The data processing flow of 3D simulation animation is shown in Figure3.Figure 3
Data processing flow of 3D simulation animation.In the process of 3D reconstruction of animation, different parts of the animation show different 3D simulation changes, and because of the local differences between each animation itself, there are also differences in the 3D simulation process between different parts and the simulation process of local features. Therefore, this study first collected a large number of animation common local data as training samples and then combined with access analysis software for local difference model reconstruction, to achieve the 3D reconstruction of different animation local posture, in order to help improve the effect and accuracy of the animation instant network communication system. In the process of local difference analysis, the value of difference degree can be calculated according to the feature factordi, so as to achieve quantitative representation [25]. In the process of 3D simulation reconstruction of virtual animation with single feature, the difference degree di can be expressed as(11)e=∑i=1n3di3+di2+7di−1di2.Considering the difference of different types of virtual animation in the feature value, the disturbance correction is made. At this time, the difference can be expressed as(12)e′=n2∑i=1n3di3+di2+7di−1∑i=1ndi2.On the contrary, in order to better analyze the local stability and standardization of animation, we also established a basic instant network communication system database in the experiment process to realize continuous updating and correction. The data processing flow is shown in Figure4.Figure 4
Data processing flow of instant network communication system database.On the contrary, because this research is based on the convolution neural networks algorithm and is oriented to 3D virtual animation, so, in the process of simulation experiment, we need to add constraints according to the feature factordi, that is, the first step is to decompose the known local 3D information of common animation and express and store it in the form of matrix. This constraint process can be expressed as(13)ε=∑i=1ndi3+3di2+2di−1di3.In order to improve the reliability of constraints, the constraint process can be expressed as follows:(14)ε′=∑i=1ndi3+3di2+2di−1/di3di2+4di−1.In this way, the local features of different animations can be dynamically changed and recognized. In this process, different fusion forms of 3D simulation models are introduced, and Gauss random matrix and Bernoulli matrix are used to store vectors to realize the recognition and storage of different types of 3D virtual animation communication information and gait matching. The simulation results of data are shown in Figure5.Figure 5
Analysis results of different 3D animation simulation data.As can be seen from Figure5, in different data, with the increase of convolution times, the coincidence degree is different. The matching degree of the first group of data is getting lower and lower, while the matching degree of the other two groups of data is getting higher and higher. The simulation process can also be characterized by the influence of characteristic factors on the correlation, that is, the quantitative evaluation of the correlation function:(15)fd=∑i=1n3di+12+2di−12ndi2.Through the analysis of correlation function, the feature vectors of different gait vectors are solved, and the point product multiplication is carried out to realize the classification and differentiation of different gait vectors. Then, local feature detection and multidimensional reconstruction of animation are carried out, including video detection, image detection, and local feature factor-tracking detection. The simulation results of three groups of data are shown in Figure6.Figure 6
Instant network communication data simulation results.From Figure6, it can be seen that different data have slight fluctuation trend. This is because, in the rapid reconstruction of 3D virtual animation dynamic instant network communication system, in the simulation process, different constraint thresholds and reconstruction models are set according to the different characteristics of 3D virtual animation, and then, these data are input into the 3D virtual animation rapid modeling system to convert the known information into data vector information and standard vector for calculation. The similarity of the fast modeling algorithms for 3D virtual animation of these vectors is analyzed. Finally, the classification analysis and reality enhancement are carried out, and the expected standard is achieved.


## 5. Result Analysis and Discussion

### 5.1. Verification Experiment Design Process of Instant Network Communication System for 3D Virtual Animation

Considering the different local features of different virtual animations, it is necessary to add different animation reconstruction constraints to different 3D virtual animation modeling algorithms before formal experiments to avoid misjudgment in the process of virtual animation modeling. Therefore, the constraints of different virtual animation instant network communication systems will be realized from the following aspects. The preliminary results of the experiment are shown in Figure7.Figure 7
The preliminary output results of the 3 groups of experiments.The change trend of experimental data is similar, and the difference is not significant. This is because the fusion form of the augmented reality 3D simulation model is processed together with the known 3D virtual animation reconstruction model (Figure7). The reconstructed 3D virtual animation is analyzed from many aspects, which enhances the local reality and realizes the dynamic tracking in the process of experiment. The space requirement of high-dimensional vector animation is eliminated for the first time, and the whole process is recorded and stored. The fast modeling algorithm of 3D virtual animation and self-learning in the process of 3D simulation are realized.


### 5.2. Experimental Results and Feedback Analysis

The experimental results are as follows. For different types of virtual animation samples, the effect of 3D reconstruction is different in the process of reconstruction for different 3D virtual animation. For example, for the virtual animation prototype in “Monkey King,” the 3D virtual animation rapid modeling algorithm will present two types of 3D virtual animation images. They are the conventional 3D virtual animation and the 3D virtual animation with the characteristics of Monkey King (with special effects). Finally, the experimental results show that the fast modeling algorithm of the instant network communication system can realize the fast modeling of 3D virtual animation and the fast transmission of instant network communication and verify the effectiveness and feasibility of the fast modeling of 3D virtual animation. Therefore, the model can also provide a basis for the analysis of fast modeling algorithm of 3D virtual animation corresponding to 3D virtual animation in various environments. The experimental results are shown in Table2.Table 2
Experimental results.
ParameterTraditional instant network communication systemInstant network communication system based on conventional neural networkInstant network communication system based on deep convolutional neural networkExperimental results0.924 ± 0.0020.931 ± 0.0020.966 ± 0.002It can be seen from Table2 that among the three different methods, the value of the experimental results obtained by the deep convolutional neural network-based instant network communication system is significantly higher than that of the other two mainstream methods, and the result is 0.966 ± 0.002, which is because the instant network communication system based on deep convolutional neural network has better and faster data analysis efficiency and data transfer rate in the process of processing data.The error analysis of the experimental results is shown in Table3.Table 3
Error analysis of experimental results.
ParameterTraditional instant network communication systemInstant network communication system based on conventional neural networkInstant network communication system based on deep convolutional neural networkTrue error0.0080.0050.003The function output image of the experimental results is shown in Figure8.Figure 8
Image of the result of the function of the experimental result.According to the results in Tables2 and 3 and Figure 8, it can be seen that the variation rules of the experimental data are basically similar and the consistency is also significantly different. In combination with the standard factor error analysis method, the error corresponding to the standard value is e = 0.012 in the quality evaluation of the 3D virtual animation fast modeling algorithm. From Tables 2 and 3, it can be seen that, in the reconstruction modeling algorithm of the signal reconstruction of the instant network communication system for 3D virtual animation, the test results of the error of the conventional instant network communication system are 0.924 ± 0.002, and error is 0.008. The test results of the fast modeling algorithm of the instant network communication system improved by the fusion form 3D simulation model are 0.966 ± 0.002. The error is 0.003, and the data variation law also meets the expected requirements.


## 5.1. Verification Experiment Design Process of Instant Network Communication System for 3D Virtual Animation

Considering the different local features of different virtual animations, it is necessary to add different animation reconstruction constraints to different 3D virtual animation modeling algorithms before formal experiments to avoid misjudgment in the process of virtual animation modeling. Therefore, the constraints of different virtual animation instant network communication systems will be realized from the following aspects. The preliminary results of the experiment are shown in Figure7.Figure 7
The preliminary output results of the 3 groups of experiments.The change trend of experimental data is similar, and the difference is not significant. This is because the fusion form of the augmented reality 3D simulation model is processed together with the known 3D virtual animation reconstruction model (Figure7). The reconstructed 3D virtual animation is analyzed from many aspects, which enhances the local reality and realizes the dynamic tracking in the process of experiment. The space requirement of high-dimensional vector animation is eliminated for the first time, and the whole process is recorded and stored. The fast modeling algorithm of 3D virtual animation and self-learning in the process of 3D simulation are realized.


## 5.2. Experimental Results and Feedback Analysis

The experimental results are as follows. For different types of virtual animation samples, the effect of 3D reconstruction is different in the process of reconstruction for different 3D virtual animation. For example, for the virtual animation prototype in “Monkey King,” the 3D virtual animation rapid modeling algorithm will present two types of 3D virtual animation images. They are the conventional 3D virtual animation and the 3D virtual animation with the characteristics of Monkey King (with special effects). Finally, the experimental results show that the fast modeling algorithm of the instant network communication system can realize the fast modeling of 3D virtual animation and the fast transmission of instant network communication and verify the effectiveness and feasibility of the fast modeling of 3D virtual animation. Therefore, the model can also provide a basis for the analysis of fast modeling algorithm of 3D virtual animation corresponding to 3D virtual animation in various environments. The experimental results are shown in Table2.Table 2
Experimental results.
ParameterTraditional instant network communication systemInstant network communication system based on conventional neural networkInstant network communication system based on deep convolutional neural networkExperimental results0.924 ± 0.0020.931 ± 0.0020.966 ± 0.002It can be seen from Table2 that among the three different methods, the value of the experimental results obtained by the deep convolutional neural network-based instant network communication system is significantly higher than that of the other two mainstream methods, and the result is 0.966 ± 0.002, which is because the instant network communication system based on deep convolutional neural network has better and faster data analysis efficiency and data transfer rate in the process of processing data.The error analysis of the experimental results is shown in Table3.Table 3
Error analysis of experimental results.
ParameterTraditional instant network communication systemInstant network communication system based on conventional neural networkInstant network communication system based on deep convolutional neural networkTrue error0.0080.0050.003The function output image of the experimental results is shown in Figure8.Figure 8
Image of the result of the function of the experimental result.According to the results in Tables2 and 3 and Figure 8, it can be seen that the variation rules of the experimental data are basically similar and the consistency is also significantly different. In combination with the standard factor error analysis method, the error corresponding to the standard value is e = 0.012 in the quality evaluation of the 3D virtual animation fast modeling algorithm. From Tables 2 and 3, it can be seen that, in the reconstruction modeling algorithm of the signal reconstruction of the instant network communication system for 3D virtual animation, the test results of the error of the conventional instant network communication system are 0.924 ± 0.002, and error is 0.008. The test results of the fast modeling algorithm of the instant network communication system improved by the fusion form 3D simulation model are 0.966 ± 0.002. The error is 0.003, and the data variation law also meets the expected requirements.


## 6. Conclusion

In recent years, 3D modeling of 3D virtual animation has been well developed, but there are still many problems in the authenticity and rapidity of the algorithm of the network instant messaging system. How to use scientific intelligent model and convolution neural network algorithm to optimize has become a research hotspot. Based on this, this paper studies the instant network communication system of 3D virtual animation and the fast modeling model of 3D simulation. This paper first analyzes the research status of reality and signal transmission speed in the three-dimensional virtual animation instant network communication system, then optimizes and improves the shortcomings of the current research hotspot instant network communication system, and optimizes it combined with the fusion morphological three-dimensional simulation model. Finally, the experimental results show that the fusion of the morphological 3D simulation model can improve the authenticity and speed of 3D virtual animation rapid modeling, and the standard protocol of the instant network communication system can be formulated according to different 3D virtual animation elements, and the accuracy of 3D modeling can reach more than 97.7%. However, this paper only studies the real-time network communication system from the perspective of enhancing realism, without considering the potential impact of local differences on animation, so the network communication system of 3D virtual animation needs further research. Therefore, future work can be normalized research and processing from the local differences and influencing factors of 3D animation and combined with quantitative evaluation criteria for analysis.



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*Source: 7277733-2021-08-29.xml*