# The Theoretical Topology and Implementation of Enterprise Social Security in the Digital Age Based on Big Data and Artificial Intelligence

Authors: Dandan Wang; Ting Lin; Hongmei Xu
Journal: Journal of Sensors
 (2022)

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## Abstract

At present, there are some problems in the social security of employees in enterprises, such as incomplete security and low reliability. According to this background, this paper studies a topological method for enterprise social security data analysis based on data adaptive analysis strategy and strong learning data stream coprocessing. According to this background, this paper studies a topology method of enterprise social security data analysis based on data adaptive analysis strategy and strong learning data flow and designed a convolutional neural network method based on mutual interference deepening strategy and advanced learning classification mode convolution neural network based on mutual interference deepening strategy and intensive learning classification pattern. According to the coverage error strategy of different types of data, the high-precision matching analysis of enterprise employee social security data is realized, and the Cartesian formula is used to correct the error and topology analysis of the analysis results. According to the experimental data and results, the topology method of enterprise social security data analysis is based on the experimental data and results. We can know that the topological method of enterprise social security data analysis is based on convolutional neural network. Enterprise social security theory can effectively improve the scope and speed of social security. This theory effectively completes the high-precision matching of different types of data and indirectly improves employees’ cognition of enterprise values.


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## Body

## 1. Introduction

At present, the whole economy is facing great adjustments and changes, and the economic risks and uncertainties faced by people are greatly increased. This uncertainty has a profound impact on people’s attitudes and consumption patterns, causing social and macroeconomic turmoil and sharp decline. As a macrostabilizer of reform, state-owned enterprises have provided social security in most reform periods, creating basic conditions for stable and rapid economic development. Social security is an inherent goal in the operation of state-owned enterprises. Therefore, it is not only a part of the economic benefits of state-owned enterprises but consumes also the resources of state-owned enterprises (in other words, it brings a burden to the production of other products of state-owned enterprises). Therefore, when evaluating the economic benefits of state-owned enterprises, it is incomplete to include specific tangible products into the output and ignore the “certainty” provided by state-owned enterprises to the society. State-owned enterprises have long provided unemployment insurance for society.At present, the social security methods adopted by enterprises mostly focus on the traditional recursive management and security strategy and rarely carry out multidimensional correlation management in combination with the needs and matching degree of enterprise employees [1]. Since entering the 21st century, a variety of intelligent technologies have also developed vigorously, especially the wide application of big data analysis and artificial intelligence technology, which has been applied on a large scale in many industries and has been able to solve some practical problems [2]. Therefore, how to combine big data and artificial intelligence technology to realize the iterative upgrading of enterprise social security methods has become the mainstream research direction [3]. At present, although the existing enterprise social security system has many high relevance application rules, there are still many deficiencies, such as inability to ensure the comprehensiveness of enterprise employee social security and low processing efficiency of enterprise employee social security [4]. According to the above research background, this research proposes a topological method for enterprise social security data analysis based on data adaptive analysis strategy and coprocessing of strong learning data flow.Aiming at the problems of poor migration application and low quality in the current enterprise social security theoretical analysis model, this paper studies the topology model of enterprise social security theory based on data adaptive analysis strategy and strong learning data flow intelligent matching algorithm; the content of this research is divided into four chapters. The first chapter introduces the analysis and application background of enterprise social security data and the innovative solutions proposed by this study. Based on data adaptive analysis strategy and strong learning data flow intelligent matching algorithm, the content of this research is divided into four chapters. Section1 introduces the analysis and application background of enterprise social security data and the innovative solutions proposed in this study; Section 2 summarizes and analyzes the research status of big data application, enterprise social security theory, and application methods at home and abroad. Section 3 is the data self-adaptive analysis strategy and strong learning data flow intelligent matching algorithm and constructs the topological analysis model of digital enterprise social security theory. Section 3 constructs a digital topology analysis model of enterprise social security theory based on data adaptive analysis strategy and strong learning data flow intelligent matching algorithm. Combined with the multi-interconnect Einstein constant analysis strategy, it constructs the enterprise social security data analysis system and evaluation index system based on artificial intelligence analysis. Section 4 analyzes and verifies the feasibility and data matching degree of the topology analysis model of enterprise social security theory constructed in this paper and draws a conclusion.Different from the traditional data stream matching type and conditional judgment-based social security analysis mode, the innovation of this paper lies in relying on the convolutional neural network algorithm, combining the data adaptive analysis strategy and the strong learning data stream intelligent matching idea, to realize the enterprise social security data. High-efficiency analysis is used to improve the utilization of social security data. On this basis, it can quickly and accurately extract high-quality and effective data information from massive dynamic social security data, realize the efficient combination of enterprise social security topology data, and use multitransformed Einstein factors to conduct quantitative analysis and high-accuracy fitting of different types of enterprise social security data types, so as to realize high-precision matching and fitting of different types of data.


## 2. Related Work

At present, the research on enterprise social security mainly focuses on theoretical research, specific case analysis, and application of migration model, while there are few research on combined innovation with artificial intelligence and big data analysis technology [5]. According to the difference of consumption record information of enterprise employees’ social security, Wei et al. proposed an adaptive matching analysis model based on difference data analysis to realize high-accuracy analysis and adaptation of different types of social security data [6]. Through experiments, Masterson et al. found that the relevance behind different types of social security data groups is significantly different and presents different change rules. Therefore, a high matching equipment data analysis model based on difference feature analysis is proposed, which can effectively improve the relevance of different types of data groups [7]. Greatbatch and Lee fused the social security data of multiple enterprises. Through specific visits to enterprise employees, they found that the employee happiness of different enterprises is closely related to the enterprise social security data and cited the high-precision analysis model based on the equipment type data analysis module. The model can effectively improve the high-accuracy matching analysis of different types of social security data [8]. Mayro proposed a method to establish an enterprise social security privacy database based on multidimensional correlation data matching and tracking analysis, which makes use of the differences of employee rank in different enterprises to realize the redistribution of packet data at all levels and realizes the high-precision analysis and mining of social security data in terms of utilization efficiency [9]. According to the idea of data combination in the traditional establishment mode of social security data collection system in a general sense, Lang et al. effectively combine the social security data types of enterprises with the high-precision matching analysis strategy of employees to achieve a high degree of unity at the data level and put forward a data matching tracking analysis model based on the edge effect development strategy [10]. By highly summarizing and unifying different types of enterprise social security data mining models, Egevad et al. try to realize the standardization, unified management, and intelligent analysis of different enterprise social security data from the perspective of intelligent distribution of the model [11]. Based on the research and analysis of social security theory in the literature, scholars such as Safarnejad L chose a strength free matching analysis model of standardized social security analysis model, which can carry out high-strength matching analysis according to the social security data types of different countries. This model can effectively classify the data ideas of different strategy types and realize the optimal utilization of different social security policies and strategies [12]. Verganti et al. manage the loyalty and happiness of enterprise employees from the types, classification, and data storage of enterprise social security storage data and realize the high-quality analysis of enterprise social security data through adaptive and integrated tracking and analysis of dynamic employee social security data changes [13]. The research results of Kai et al. show that the enterprise social security information interaction method based on the two in one coupling model of data acquisition and data analysis has higher efficiency in enterprise data processing and can improve the accuracy of at least 10% compared with the traditional social security data management method [14]. In order to improve the stability and authenticity of enterprise employee social security data, Liu et al. have carried out various high-dimensional calculations on different enterprise employee social security data to achieve high-precision acquisition and discrete processing of different types of data, which can effectively improve the matching rate of different types of data groups, but there are high requirements for the scope of application [15]. Goodarzian et al. have proved through experiments that the social security data groups of different enterprises in different regions have obvious characteristics of simplification and difference and can efficiently carry out high-precision analysis on different types of enterprise data guarantee groups [16].To sum up, it can be seen that there are some problems in the theoretical research of enterprise social security, such as lagging simplification method (uneven matching of valid data) [17–19]. Although current scholars have achieved preliminary innovative applications in the data application of social security and the application of multidimensional change pattern matching, there are few research results in practical application and artificial intelligence technology iterative analysis, and there are no demonstrative achievements in the normalization and innovative application of corporate social security data, similar to two-dimensional spatial decomposition strategy [20, 21].


## 3. Methodology

With the iterative updating of different artificial intelligence technologies in recent years, it is possible to apply big data analysis technology in different industries [22]. In the field of big data analysis technology, different analysis strategies have different advantages. As a typical intelligent analysis method, neural network algorithm plays an important role in the development needs of enterprises [23]. However, the traditional neural network algorithm is not suitable for solving the application problems of all industries. Some scholars have improved the algorithm for different types of data applications. Therefore, intelligent collaborative algorithms based on data adaptive analysis strategies and strong learning data stream collaborative processing came into being. The convolution neural network algorithm not only has the advantages of fuzzy class analysis of neural network algorithm; the accuracy and efficiency of data analysis have also been greatly improved [24]. The theory of enterprise social security has also been constantly updated with the real needs of society, so there is also a large room for improvement. How to apply the fast-growing big data and artificial intelligence technology in recent years to the theory of enterprise social security and realize the intelligent analysis of social security data is the current research hotspot [25]. The principle of intelligent collaborative algorithm based on data adaptive analysis strategy and strong learning data stream collaborative processing is shown in Figure 1.Figure 1
Operation principle of intelligent collaborative algorithm based on data adaptive analysis strategy and strong learning data stream collaborative processing.In the process of coupling analysis of enterprise social security data, convolutional neural network will evolve into various types of business support data groups through coupling management of different types of social security data groups. When different data groups are identified with high accuracy by compiler, their internal relevance also shows a high change law. Therefore, it is necessary to carry out iterative update processing. After different degrees of data iterative processing, its internal relevance data groups will show different types of high-accuracy translation efficiency, so its internal interconnection influence will be characterized, and different types of data groups will realize relevance analysis according to different digital features. Therefore, after perfecting the social security theory database of many enterprises, its internal relevance presents different expression characteristics. Then, according to its internal local efficiency data group, it realizes the high-accuracy information extraction of different types of data groups and realizes the local optimization and feature extraction of data.


### 3.1. Establishment Process of Multiple Grey Convolution Neural Network Model Based on Big Data

For the correlation analysis of various data groups, the selection standard to eliminate unnecessary data in the data group is the result of correlation analysis. In the process of analyzing enterprise social security data, we can eliminate unnecessary data in the data group through the correlation analysis of various data groups. Then, according to the characteristics of different types of differentiation, realize the high-intensity analysis of its internal data. After the analysis, according to its different correlation types, realize the high-intensity disturbance analysis of different data groups, then match the correlation degree of these effective information data groups, and finally, form the correlation data matching with differentiation characteristics. Figure2 shows the data flow operation process of the social security topology analysis model based on the data adaptive analysis strategy and the strong learning data flow cooperative processing intelligent collaborative algorithm.Figure 2
Data flow operation process of social security topology analysis model based on data adaptive analysis strategy and strong learning data flow cooperative processing intelligent collaborative algorithm.The process of data processing is divided into several stages. The first stage is to classifyn enterprise social security data groups. These data groups are original data groups and have obvious characteristics—each data group has m correlation data nodes with different dimensions. After high-intensity matching of these data nodes, the sequence can be obtained as follows:
(1)Y1=1+x11,x12,⋯,x1nx11+x12+⋯+x1n,Y2=Y1+1+x21,x22,⋯,x2nx21+x22+⋯+x2n,Y3=Y2+1+x31,x32,⋯,x3nx31+x32+⋯+x3n,YM=Yn+⋯+1+x31,x32,⋯,xmnx31+x32+⋯+xmn.Therefore, when topological analysis is required for different types of data, only the internal weight coefficientℵ of Yi and Yj needs to be calculated, and the calculation formula is
(2)ℵ=1+Ym+Ym+1/Ym+Ym−1Ym−Ym−1.Then, according to the weight coefficient index, the coupling degree of different types of social security data groups is solved, and the coupling degree function is(3)Ux=ℵx2+ℵ+1x4+1ℵx2+ℵ−1x3+ℵ−2x4+2ℵii, where ℵii=1;i=1,2,⋯,m. After completing the above analysis, it is also necessary to analyze the high-value matching degree according to the correlation degree of different enterprise types and evolve it into a low-latitude social security data group index. The simulation results are shown in Figure 3.Figure 3
The corresponding simulation analysis results under the low-latitude social security data set indicators.The simulation results of level 1 artificial intelligence matching analysis for different types of enterprise social security data types are shown in Figure4.Figure 4
The corresponding simulation analysis results under the social security data group index under the first-level line change.The simulation results of two-dimensional and three-level artificial intelligence matching analysis for different types of enterprise social security data types are shown in Figure5.Figure 5
The corresponding simulation analysis results under the social security data group index under the second-level discrete behavior change.The simulation results of three-dimensional 4-level artificial intelligence matching analysis for different types of enterprise social security data types are shown in Figure6.Figure 6
The corresponding simulation analysis results under the social security data group index under the three-level aggregated behavior change.It can be seen from the four groups of data results in Figures3–6 that after analyzing different types of enterprise social security data groups according to different types of high-value matching, combined with convolution neural network algorithm, when using big data and artificial intelligence topology analysis strategy for high-value matching analysis, it is also necessary to perform association calculation and processing for different types of data groups (0/1/2/3 level AI high matching factor). Due to the need to combine high intensity and high factor in the matching analysis of social security data, the data model of these factors will have strong internal correlation, matching degree, and disturbance of value function. Therefore, with the change of topology analysis times, the redundant information in the internal authentication database will be dynamically updated with the incompleteness of topology. The corresponding convolution network marking function in this process is recorded as Ijkx:
(4)Ijkx=x−xjk1/xjk3+xjk2+x+xjk1/xjk2+xjk1xjk2+xjk1+xjk3.xjk∗ is the topology node of social security theory. After the calculation, it is also necessary to carry out high-precision topology analysis for different types of social security data types. In this stage, the precision extreme value measurement function Px is required, and its expression is
(5)Px=ℵxjk4−xxjk4−xjk3+ℵxjk4−xxjk4−xjk3.In addition, after analyzing the topological structure of different types of enterprise social security theory, it is also necessary to measure and score different types of high-value topological data. The expression of continuous measurement function is formula (6), and the expression of discrete measurement function is formula (7).
(6)Lx=Px+xjk4−x/xjk4−xjk2Px+Px−1,(7)Kx=Ijkx+PxIjkx+P2x‐1+x−xjk1xjk2−xjk1.xjk∗ is the topology node of social security theory.


### 3.2. Data Processing Process of Topological Sparse Model of Enterprise Social Security Theory Based on Convolutional Neural Network

In this model, in order to carry out variable weight analysis on different types of enterprise social security data, different types of enterprise social security databases need to be updated and maintained regularly. The iterative calculation and update of the weight coefficients are realized by determining the data adaptive analysis strategy and the strong learning data flow high-strength coincidence variables. The high-intensity analysis and iterative calculation of different types of data groups can be realized from the two-dimensional level. The high-dimensional variable weight coefficientI required in this process can be expressed as
(8)I=∑j−1mIjkxij‐1+ℵjk+∑j=1mIjkxij⋅ℵjkIjkxij+ℵjk.Specifically, there are(9)Ri2=∑j=1mIj1xij−1⋅ℵj1,∑j=1mIj2xij−2⋅ℵj2,⋯,∑j=1mIjsxij−s⋅ℵjs.The above formula is the high-dimensional variable weight coefficient in the topology analysis structure, and its corresponding high-intensity expression function is(10)ℝ=ℵjk+RjkIjkxij+ℵjk.After completing the above links, in order to further carry out Gaussian mixture analysis on different types of enterprise social security data and realize the analysis of high accuracy and low error rate based on Einstein factor, Figure7 shows the analysis and simulation results and changing trends of the social security weight coefficient groups that change in different modes.Figure 7
Correspondence analysis of social security weight coefficient groups that change in different modes and simulation results of changing trends.It can be seen from Figure7 that in the process of analyzing enterprise social security data, with the increase of the number of topological structures, the coupling functions corresponding to different types of influencing factors also show different change laws, because different types of social security theory databases have significant differences in characteristics. The data analysis data of different dimensions will show high-intensity extreme value changes of different values, and the corresponding extreme value change function can be expressed as Ax.
(11)Ax=∑j=1mYj1xj+∑j=1mIj1xjRj1+ℵj1.The result under the analysis strategy after multidimensional discretization can be expressed asA′x and
(12)A′x=∑j=1mYj1xj+∑j=1mIj1xjRj1Ax.After completing this link, it is also necessary to evaluate the extreme value of different types of high-accuracy social security value, and its internal correlation also needs to be calibrated. Therefore, in the process of fuzzy search and screening of unknown corporate social security data targets (newly collected corporate employee social security information), the corresponding discrete discriminant function isDx and the expression is
(13)Dx=βj1+R¯∑j=1mxj‐x¯∑j=1mRj‐R¯+ℵj1x¯,where βj1 is the topological reinforcement coefficient of social security.


## 3.1. Establishment Process of Multiple Grey Convolution Neural Network Model Based on Big Data

For the correlation analysis of various data groups, the selection standard to eliminate unnecessary data in the data group is the result of correlation analysis. In the process of analyzing enterprise social security data, we can eliminate unnecessary data in the data group through the correlation analysis of various data groups. Then, according to the characteristics of different types of differentiation, realize the high-intensity analysis of its internal data. After the analysis, according to its different correlation types, realize the high-intensity disturbance analysis of different data groups, then match the correlation degree of these effective information data groups, and finally, form the correlation data matching with differentiation characteristics. Figure2 shows the data flow operation process of the social security topology analysis model based on the data adaptive analysis strategy and the strong learning data flow cooperative processing intelligent collaborative algorithm.Figure 2
Data flow operation process of social security topology analysis model based on data adaptive analysis strategy and strong learning data flow cooperative processing intelligent collaborative algorithm.The process of data processing is divided into several stages. The first stage is to classifyn enterprise social security data groups. These data groups are original data groups and have obvious characteristics—each data group has m correlation data nodes with different dimensions. After high-intensity matching of these data nodes, the sequence can be obtained as follows:
(1)Y1=1+x11,x12,⋯,x1nx11+x12+⋯+x1n,Y2=Y1+1+x21,x22,⋯,x2nx21+x22+⋯+x2n,Y3=Y2+1+x31,x32,⋯,x3nx31+x32+⋯+x3n,YM=Yn+⋯+1+x31,x32,⋯,xmnx31+x32+⋯+xmn.Therefore, when topological analysis is required for different types of data, only the internal weight coefficientℵ of Yi and Yj needs to be calculated, and the calculation formula is
(2)ℵ=1+Ym+Ym+1/Ym+Ym−1Ym−Ym−1.Then, according to the weight coefficient index, the coupling degree of different types of social security data groups is solved, and the coupling degree function is(3)Ux=ℵx2+ℵ+1x4+1ℵx2+ℵ−1x3+ℵ−2x4+2ℵii, where ℵii=1;i=1,2,⋯,m. After completing the above analysis, it is also necessary to analyze the high-value matching degree according to the correlation degree of different enterprise types and evolve it into a low-latitude social security data group index. The simulation results are shown in Figure 3.Figure 3
The corresponding simulation analysis results under the low-latitude social security data set indicators.The simulation results of level 1 artificial intelligence matching analysis for different types of enterprise social security data types are shown in Figure4.Figure 4
The corresponding simulation analysis results under the social security data group index under the first-level line change.The simulation results of two-dimensional and three-level artificial intelligence matching analysis for different types of enterprise social security data types are shown in Figure5.Figure 5
The corresponding simulation analysis results under the social security data group index under the second-level discrete behavior change.The simulation results of three-dimensional 4-level artificial intelligence matching analysis for different types of enterprise social security data types are shown in Figure6.Figure 6
The corresponding simulation analysis results under the social security data group index under the three-level aggregated behavior change.It can be seen from the four groups of data results in Figures3–6 that after analyzing different types of enterprise social security data groups according to different types of high-value matching, combined with convolution neural network algorithm, when using big data and artificial intelligence topology analysis strategy for high-value matching analysis, it is also necessary to perform association calculation and processing for different types of data groups (0/1/2/3 level AI high matching factor). Due to the need to combine high intensity and high factor in the matching analysis of social security data, the data model of these factors will have strong internal correlation, matching degree, and disturbance of value function. Therefore, with the change of topology analysis times, the redundant information in the internal authentication database will be dynamically updated with the incompleteness of topology. The corresponding convolution network marking function in this process is recorded as Ijkx:
(4)Ijkx=x−xjk1/xjk3+xjk2+x+xjk1/xjk2+xjk1xjk2+xjk1+xjk3.xjk∗ is the topology node of social security theory. After the calculation, it is also necessary to carry out high-precision topology analysis for different types of social security data types. In this stage, the precision extreme value measurement function Px is required, and its expression is
(5)Px=ℵxjk4−xxjk4−xjk3+ℵxjk4−xxjk4−xjk3.In addition, after analyzing the topological structure of different types of enterprise social security theory, it is also necessary to measure and score different types of high-value topological data. The expression of continuous measurement function is formula (6), and the expression of discrete measurement function is formula (7).
(6)Lx=Px+xjk4−x/xjk4−xjk2Px+Px−1,(7)Kx=Ijkx+PxIjkx+P2x‐1+x−xjk1xjk2−xjk1.xjk∗ is the topology node of social security theory.


## 3.2. Data Processing Process of Topological Sparse Model of Enterprise Social Security Theory Based on Convolutional Neural Network

In this model, in order to carry out variable weight analysis on different types of enterprise social security data, different types of enterprise social security databases need to be updated and maintained regularly. The iterative calculation and update of the weight coefficients are realized by determining the data adaptive analysis strategy and the strong learning data flow high-strength coincidence variables. The high-intensity analysis and iterative calculation of different types of data groups can be realized from the two-dimensional level. The high-dimensional variable weight coefficientI required in this process can be expressed as
(8)I=∑j−1mIjkxij‐1+ℵjk+∑j=1mIjkxij⋅ℵjkIjkxij+ℵjk.Specifically, there are(9)Ri2=∑j=1mIj1xij−1⋅ℵj1,∑j=1mIj2xij−2⋅ℵj2,⋯,∑j=1mIjsxij−s⋅ℵjs.The above formula is the high-dimensional variable weight coefficient in the topology analysis structure, and its corresponding high-intensity expression function is(10)ℝ=ℵjk+RjkIjkxij+ℵjk.After completing the above links, in order to further carry out Gaussian mixture analysis on different types of enterprise social security data and realize the analysis of high accuracy and low error rate based on Einstein factor, Figure7 shows the analysis and simulation results and changing trends of the social security weight coefficient groups that change in different modes.Figure 7
Correspondence analysis of social security weight coefficient groups that change in different modes and simulation results of changing trends.It can be seen from Figure7 that in the process of analyzing enterprise social security data, with the increase of the number of topological structures, the coupling functions corresponding to different types of influencing factors also show different change laws, because different types of social security theory databases have significant differences in characteristics. The data analysis data of different dimensions will show high-intensity extreme value changes of different values, and the corresponding extreme value change function can be expressed as Ax.
(11)Ax=∑j=1mYj1xj+∑j=1mIj1xjRj1+ℵj1.The result under the analysis strategy after multidimensional discretization can be expressed asA′x and
(12)A′x=∑j=1mYj1xj+∑j=1mIj1xjRj1Ax.After completing this link, it is also necessary to evaluate the extreme value of different types of high-accuracy social security value, and its internal correlation also needs to be calibrated. Therefore, in the process of fuzzy search and screening of unknown corporate social security data targets (newly collected corporate employee social security information), the corresponding discrete discriminant function isDx and the expression is
(13)Dx=βj1+R¯∑j=1mxj‐x¯∑j=1mRj‐R¯+ℵj1x¯,where βj1 is the topological reinforcement coefficient of social security.


## 4. Result Analysis and Discussion

### 4.1. Experimental Design and Data

In order to analyze the coupling and real effect of the corporate social security data analysis topology method based on data adaptive analysis strategy and strong learning data stream coprocessing in the actual process of social security data processing, it is necessary to combine different types of high-intensity enterprise social security data groups for coupling management analysis, so the corresponding confirmatory experiment is designed. In the experiment process, the completely public data is used, and the discrete random change rule is adopted, and it is input into the original database enterprise employee social security information data. In the process of coupling analysis of different types of enterprise social security data groups, its targeted evaluation indicators also need to be set. Therefore, this study is screened according to the existing international standards. Finally, 25 indicators are selected to evaluate the randomness and accuracy of the topology of corporate social security theory. During the experiment, the database information of the enterprise is related to different types of high-strength continuity characterization functions, and industries are selected as the experimental verification data group for topology analysis and verification. The experimental analysis results are shown in Figure8.Figure 8
Topological analysis to verify the experimental results of employee social insurance data of 4 types (8 groups, 2 groups for each type) of enterprises.It can be seen from Figure8 that during the experiment, different groups have different accuracies under different types of data processing, their corresponding high-intensity function values have strong stability and gradual change, and their internal correlation also shows different change laws. This is because their corresponding data analysis rules will also change under large data and artificial intelligence analysis technology. Therefore, its internal relevance can be displayed through the artificial intelligence data analysis system, and so can the internal data relevance.


### 4.2. Experiment Result Analysis of Enterprise Social Security Data Analysis Topology Method Based on Data Adaptive Analysis Strategy and Strong Learning Data Flow Coprocessing

From the experimental results in Figure8 and the accuracy analysis results of social security data in Figure 9, it can be seen that there is little difference in volatility after analyzing the social security data of different enterprises used in the experimental process. In addition, different types of data groups also have great differences in the correlation degree and matching degree. In addition, among multiple evaluation quantifications for perturbation analysis on different data sets, there will be obvious differences in the internal pertinence change and correlation matching degree, which will lead to obvious fluctuations in the correlation data between data groups.Figure 9
Data analysis of experimental results.Therefore, it can be seen from the above analysis and experimental results that in the process of processing experimental data, this study combines data adaptive analysis strategy and adopts strong learning data flow collaborative processing method based on iterative data high-dimensional analysis based on data adaptive analysis strategy intelligent algorithm, which can schedule, match, and track the enterprise social security data information of different industries from any angle, and through the coupling and error of different data and the stability differences within the enterprise, the internal relevance platform of data groups can be guaranteed to realize the high-intensity analysis and data representation of different data groups. Then, according to the vector difference, length difference, and other dimensional information of data groups in different columns in the enterprise social security database, the iterative hierarchical division and high-intensity planning of different social security data are realized. Finally, through the neural network algorithm and artificial intelligence analysis technology, the high-accuracy analysis process of social security data information in different dimensions is realized. Finally, combined with the differences of different types of data groups, the iterative classification is carried out to realize the intelligent adaptive law of social security data in different enterprises, combined with the sophisticated analysis of social security enterprise data groups, analyze the probability distribution and error coupling degree of the internal social security data, realize the topological change analysis and prediction of the enterprise social security data type, and then complete the internal representation of the information contained in the enterprise social security data and the rapid analysis of the correlation degree.


## 4.1. Experimental Design and Data

In order to analyze the coupling and real effect of the corporate social security data analysis topology method based on data adaptive analysis strategy and strong learning data stream coprocessing in the actual process of social security data processing, it is necessary to combine different types of high-intensity enterprise social security data groups for coupling management analysis, so the corresponding confirmatory experiment is designed. In the experiment process, the completely public data is used, and the discrete random change rule is adopted, and it is input into the original database enterprise employee social security information data. In the process of coupling analysis of different types of enterprise social security data groups, its targeted evaluation indicators also need to be set. Therefore, this study is screened according to the existing international standards. Finally, 25 indicators are selected to evaluate the randomness and accuracy of the topology of corporate social security theory. During the experiment, the database information of the enterprise is related to different types of high-strength continuity characterization functions, and industries are selected as the experimental verification data group for topology analysis and verification. The experimental analysis results are shown in Figure8.Figure 8
Topological analysis to verify the experimental results of employee social insurance data of 4 types (8 groups, 2 groups for each type) of enterprises.It can be seen from Figure8 that during the experiment, different groups have different accuracies under different types of data processing, their corresponding high-intensity function values have strong stability and gradual change, and their internal correlation also shows different change laws. This is because their corresponding data analysis rules will also change under large data and artificial intelligence analysis technology. Therefore, its internal relevance can be displayed through the artificial intelligence data analysis system, and so can the internal data relevance.


## 4.2. Experiment Result Analysis of Enterprise Social Security Data Analysis Topology Method Based on Data Adaptive Analysis Strategy and Strong Learning Data Flow Coprocessing

From the experimental results in Figure8 and the accuracy analysis results of social security data in Figure 9, it can be seen that there is little difference in volatility after analyzing the social security data of different enterprises used in the experimental process. In addition, different types of data groups also have great differences in the correlation degree and matching degree. In addition, among multiple evaluation quantifications for perturbation analysis on different data sets, there will be obvious differences in the internal pertinence change and correlation matching degree, which will lead to obvious fluctuations in the correlation data between data groups.Figure 9
Data analysis of experimental results.Therefore, it can be seen from the above analysis and experimental results that in the process of processing experimental data, this study combines data adaptive analysis strategy and adopts strong learning data flow collaborative processing method based on iterative data high-dimensional analysis based on data adaptive analysis strategy intelligent algorithm, which can schedule, match, and track the enterprise social security data information of different industries from any angle, and through the coupling and error of different data and the stability differences within the enterprise, the internal relevance platform of data groups can be guaranteed to realize the high-intensity analysis and data representation of different data groups. Then, according to the vector difference, length difference, and other dimensional information of data groups in different columns in the enterprise social security database, the iterative hierarchical division and high-intensity planning of different social security data are realized. Finally, through the neural network algorithm and artificial intelligence analysis technology, the high-accuracy analysis process of social security data information in different dimensions is realized. Finally, combined with the differences of different types of data groups, the iterative classification is carried out to realize the intelligent adaptive law of social security data in different enterprises, combined with the sophisticated analysis of social security enterprise data groups, analyze the probability distribution and error coupling degree of the internal social security data, realize the topological change analysis and prediction of the enterprise social security data type, and then complete the internal representation of the information contained in the enterprise social security data and the rapid analysis of the correlation degree.


## 5. Conclusion

At present, there are some problems in the social security of employees in enterprises, such as incomplete security and low reliability. Based on this, this paper studies a new model of topology optimization of enterprise social security theory based on big data and artificial intelligence data analysis strategy and designs an intelligent extraction model of enterprise employee social security data based on the coprocessing method based on data adaptive analysis strategy and strong learning data stream of different types of data, the high-precision matching analysis of enterprise employee social security data is realized, and the Cartesian formula is used to correct the error and topology analysis of the analysis results. The results show that the topology optimization model with continuous digital matching pursuit and perceptual analysis strategy enterprise social security theory based on convolutional neural network can effectively improve the scope and speed of social security, effectively complete the high-accuracy matching of different types of data, and indirectly improve employees’ recognition of enterprise values. Compared with the normalized and discrete traditional social security, treatments have limitations, and the innovation of this paper is to combine the big data analysis strategy with the artificial intelligence analysis strategy and use the convolution neural network algorithm to intelligently analyze the enterprise social security data information. On this basis, combined with different types of enterprise social security data, intelligent matching is carried out according to its internal relevance and coupling, and the factors affecting the topology accuracy of social security data are customized. However, the enterprise social security data analysis model proposed in this study can only conduct high-intensity value analysis on the internal relevance of data types and does not take other potential influencing factors in the social security database into account. Therefore, further research can be carried out in terms of redundancy and robustness.



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*Source: 7814886-2022-06-22.xml*