Research on the Optimization of National Governance System Based on Data Science under the Perspective of Marxism

In order to truly realize social harmony and stability and the country’s long-term peace and security, it is still necessary to rely on the system, on a high level of competence in governance and on a high-quality cadre of cadres. In order to achieve long-term peace and stability in the country, the national governance system needs to be continuously optimized.

As the main body of social governance, the state has the highest power and authority. National governance is a complex and important issue, which involves the operation of state power, the formulation and implementation of public policy, the distribution of social resources, the protection of civil rights and other aspects [1-3]. And the national governance system is the system of managing the country under the leadership of the party, including the institutional mechanisms, laws and regulations, and institutional procedures in various fields such as economy, politics, culture, society, ecological civilization, and party building, i.e., a set of closely linked and mutually coordinated national system [4-5]. The task of national governance is to ensure the stable, orderly and efficient operation of the national governance system through scientific methods and means [6-7]. The national governance system includes government power supervision and control, market economic system, rule of law construction, socialist democracy and comprehensive rule of law. In the aspect of government power supervision and control, the state needs to establish an effective supervisory body to strengthen the supervision and constraints on government behavior [8-10]. In terms of the market economic system, the state needs to improve the market mechanism, break the administrative monopoly, and promote fair competition in the market and the effectiveness of resource allocation [11-13]. In the construction of the rule of law, the state needs to improve the system of laws and regulations to ensure the fairness, stability and predictability of the law [14-15]. In terms of socialist democracy and comprehensive rule of law, the state needs to strengthen the construction of the people’s congress system, enhance the scientific nature of national decision-making and the reflection of public opinion, and fully respect and safeguard the basic rights of the people in the process of governance [16-17]. National governance capacity is the ability to use the national system to manage all aspects of social affairs, including reform, development, stability, internal affairs, foreign affairs and national defense, and to govern the Party, the country and the military [18-19]. Only with a good national governance system can we improve our governance capacity, and only by improving our governance capacity can we give full play to the effectiveness of our national governance system. China’s national governance system emphasizes people-centeredness and puts forward the development concept that people’s interests are paramount.

In the era of abundant digital data, the national governance system should also be aligned with data science to provide more reference value for its optimization, so that the national governance system can continuously meet the people’s needs for a better life and achieve social development and progress through continuous improvement and innovation [20-22].

This study mainly uses the method of literature research, visit survey method, questionnaire survey method, supplemented by multidisciplinary cross-study method, to carry out research on the optimization of the national governance system under the perspective of Marxism. The governance system optimization strategy based on data science in this paper takes digital technology as the key means, clarifies the target demand for overall digital governance, and carries out the optimal allocation of digital resources.Combined with the fuzzy hierarchical analysis method to obtain key remediation indicators, to improve the effectiveness of governance system optimization.

2

Research on a Marxist-based governance system for data science

2.1

Data Science Governance System Research Methodology

2.1.1

Literature research method

By comprehensively searching for existing literature and books and some archival materials on the optimization of the national governance system of data science and other aspects, including both the databases of university libraries and local libraries, local records and book materials, as well as the use of Internet resources to search and collect relevant content, through the sorting, summarizing and analyzing of this literature and information, we have done a good job in preparing a detailed reserve of information for the research of this paper.

2.1.2

Visiting survey method

Taking place A as an example, the local residents and enterprises were visited and researched simultaneously. Several times in-depth investigations were conducted on multiple streets and community work offices, and many field visits were made. Focusing on the research theme of this paper, the pre-designed interview outline, and the local community street cadres and residents in the jurisdiction of the in-depth conversation, to obtain first-hand information.

2.1.3

Questionnaire method

This paper focuses on the effectiveness of grassroots social governance, the main practices and experiences, and the exploration of optimization paths in place A. The information was collected through electronic questionnaires and questionnaires distributed to residents in the district during visits.Relevant information was collected through electronic questionnaires and questionnaires distributed to residents in the district during visits.

2.1.4

Multidisciplinary approach to research

By adopting the application of multidisciplinary range of theoretical knowledge, the research on the optimization strategy of the national governance system by using data science mainly involves Marxist theory, combining data science as well as the related fields of sociology, political science and management science to carry out a holistic, scientific and comprehensive exploratory research.

2.2

Strategies for Optimizing the National Governance System for Data Science

Data science governance combines the concept of governance with digital information technology, and data science governance includes the management of digital information, the construction and operation of a governance system based on digital information, as well as the overall process of utilizing digital information for the management of the economy, society and people’s livelihoods. Data science governance can be regarded as a new type of governance system, consisting of three core elements: the digital government system, digital technology infrastructure and technical norms, and the mechanism for the development of the digital economy, society and people’s livelihoods.

2.2.1

Establishment of the goal of holistic intellectual governance

The pursuit of overall intelligent governance implies the combination of comprehensive governance and intelligent governance in order to promote A’s realization of intelligent governance. Intelligent governance, on the other hand, with the support and empowerment of digital technology, helps the governing bodies to sort, integrate, analyze and satisfy the governance needs of Place A in a more detailed way through the extensive and in-depth application of digital technology and full mastery of digital platforms by each governing body, breaking through the multiple limitations of digital transmission and sharing.

According to the requirements of overall intelligent governance, the goal of scientific governance of data in Place A is to achieve information sharing among grassroots government departments, government departments and higher government departments, government and village-level organizations, and village-level organizations and residents. Through the interconnection and sharing of information and data for mutual benefit, the existing information silo effect will be broken, information and data will be corrected and improved, data validity will be enhanced, and the dilemma of decentralized and fragmented governance will be overcome.

2.2.2

Optimized allocation of resources

As a new model of governance, the scientific governance of data in place A fully leverages the advantages of information technology and overcomes the limitations of traditional governance.It provides village-level organizations with technological governance resources and means to target residents’ problems in their daily lives, and improves their ability to respond to residents’ demands and provide public service.

The data science governance model focuses not only on improving governance efficiency in Place A, but also on protecting and developing the social and cultural environment in Place A. With the help of digital technology, in-depth research and monitoring of the history and culture, ecological environment, and industrial structure of Place A are carried out, so as to provide scientific basis and support for the sustainable development of Place A.

In addition, the scientific management of land A data assists in optimizing the allocation of resources and improving the targeting of land A governance. Through precise identification and localization of the problems in place A, limited governance resources can be more rationally allocated to various fields, thus solving problems more effectively.

2.3

Construction of evaluation indicators for data science governance system

As shown in Table 1, the construction of the data science governance optimization evaluation index system should firstly have an overall grasp of the core meaning of data science governance and its characteristics.Secondly, it is necessary to consider the guidelines, policies, and guidance on data science governance formulated by the state. Once again, it is necessary to follow the objective law of the development of data science governance, taking into account the characteristics of the optimization process of A place such as differences and instability, we regard it as a comprehensive system and conduct in-depth research on the actual development situation and relevant practical experience as a basis for jointly constructing the evaluation index system.

Table 1.

Digital national governance evaluation index system

Secondary indicator	Tertiary index	Four level index
Digital infrastructure	Network infrastructure	Internet penetration
		4G coverage
		Cable length
	Terminal equipment	Smartphone ownership
		Broadband access users
		Computer ownership
	Information capital investment	Investment in scientific research and technology services
	Information capital investment	Transportation and other investments
Agricultural digitization	Agricultural production digitization	The application rate of information technology in the planting industry
	Agricultural production digitization	The application rate of information technology in animal husbandry
	Agricultural business digitization	Product network sales rate
	Agricultural business digitization	The digital level of agricultural processing equipment
	Agricultural management digitization	Product production system management information level
	Agricultural management digitization	The level of information of agricultural administrative law enforcement
	Agricultural service digitization	The establishment of the rural digitization service organization
	Agricultural service digitization	Information service technician
Governance digitization	Industry digitization	Number of e-commerce enterprises
		Intelligent tourism revenue
		Digital financial institution
	Cultural digitization	Library number
	Cultural digitization	Digital library construction number
	Services digitization	Online number
		Efficiency of Internet services
		Administrative coverage
	Public service digitization	Number of digital medical institutions
		The penetration rate of multimedia classroom
		Medical insurance coverage
	Environmental monitoring digitization	Pollution emission detection system coverage
	Environmental monitoring digitization	Access to toilets

2.3.1

Design of the indicator system

1)

Digital infrastructure

Information infrastructure is the digital platform for optimizing Place A. Indicators in terms of digital infrastructure are used to measure the optimization and development of digital information infrastructure in Place A during the optimization process of data science governance, and the optimization of digital infrastructure in Place A should focus on the common construction and sharing, and continuously strengthen the platform interoperability and data sharing.

Digital infrastructure mainly includes the following aspects: network infrastructure, terminal equipment, and capital investment in informationization. Network infrastructure is an important carrier for residents of A to connect with the outside world, and network infrastructure mainly regulates the common construction and sharing of fiber optic networks, mobile broadband networks, telephones, broadcasting and television, and agricultural private networks in rural areas, as well as basic network services and the integration of cross-network services.

2)

Agricultural informatization

Agricultural informatization refers to the comprehensive application and development of modern information technology in the field of agriculture, so that it penetrates into all specific aspects of agriculture, such as agricultural production, agricultural management, agricultural management and agricultural services. The development and growth of the organization can, to some extent, promote the development of data science governance. Whether or not there is a well-staffed agricultural and rural informatization management service institution determines whether or not it can provide residents with convenient and fast information and appropriate help in the optimization process, and it is also an important indicator for evaluating the optimization systematic and complete. The indicators of agricultural informatization selected in this paper include four three-level indicators of agricultural production informatization, agricultural management informatization, agricultural management informatization and agricultural service informatization.

3)

Digitization of governance

In the research process, it is first necessary to investigate and analyze the selected indicators, and then determine the weight value of each indicator based on the results of the survey. With the continuous advancement of the development strategy of digital governance in Place A, modern information technologies such as microelectronics technology, sensing technology, reality technology, Internet of Things technology and other modern information technologies are gradually changing the various fields and links of traditional agriculture. Therefore, in the context of the new period, it is of great significance to study and analyze the digital development of Place A. The digitization scope of Place A includes a variety of aspects, including digitizing the industry, culture, governance, public services, and environmental monitoring of Place A.The digitalization of governance in this paper includes the digitization of industry, culture, governance, public services, and environmental monitoring.The digitization indicators for governance selected in this paper include five three-level indicators: industry digitization, culture digitization, service digitization, public service digitization, and environment monitoring digitization.

2.3.2

Data sources

This paper selects the relevant data from 2014 to 2023 to evaluate the effectiveness of the pilot optimization of the scientific governance system of national data in place A. The data sources of all indicators in this paper mainly consist of the following two parts: i. Public government documents. ii. This part mainly refers to the National Economic and Social Development Statistics Bulletin (2014-2023), Annual Report on Government Information Disclosure, Statistical Yearbook, Strategic Plan for Revitalization of Place A, and other public documents of other government departments in multiple counties in Place A. Second, it was collected and obtained through telephone calls, interviews and field visits to relevant staff, calculations and other means. In rural areas, there are some problems with missing data, which are dealt with by using the mean replacement method.

2.4

Evaluation of optimization effect based on fuzzy hierarchy analysis

2.4.1

Methodological process

The core of the fuzzy hierarchical analysis method [23-24] is to construct a fuzzy consistency judgment matrix, which is more consistent with the decision-making goal in consistency than the simple hierarchical analysis method, and the realization process is more convenient and quick. The specific process of fuzzy hierarchical analysis is shown in Figure 1.

According to the above flow chart, we can know the specific operation steps of fuzzy hierarchical analysis:

1) Determine the corresponding indicators according to the problem of the research objectives, and construct a scientific and reasonable indicator system.

2) Compare the indicators based on the scale, and each level of indicators corresponds to its judgment matrix.

3) Normalize the judgment matrix and calculate the eigenvectors to get the layered weights, and check whether each judgment matrix meets the consistency.

4) Calculate the combination weights according to the layered weights and test their consistency.

5) Determine the weights.

2.4.2

Fuzzy matrices

1)

Definition of fuzzy matrix

For matrix B = (b_ij)_m×n, if it is consistent with 0 ≤ b_ij ≤ 1(i = 1,2,…,m;j = 1,2,…,n), the matrix is said to be a fuzzy matrix [25].

2)

Definition of fuzzy complementary judgment matrix

We know that each factor is qualitative, if we do not use a certain method of pre-processing, it is impossible to use mathematical tools to calculate, so that the solution of the model can not be completed, and the relationship between the various factors is also very difficult to study, so I use quantitative analysis of the factors to pre-processing, mainly in order to give each factor a degree of importance to the fuzzy analysis, at this time, a fuzzy judgment matrix will be obtained A = (a_ij)_m×n. If this matrix obtained meets the following requirements, it is called a fuzzy complementary judgment matrix.

Requirement: (1) $a_{i i} = 0.5 (i = 1, 2, \dots, n)$ (2) $a_{i i} + a_{i j} = 1 (i = 1, 2, \dots, n)$

3)

Establishment of a weighting judgment matrix

Therefore it is necessary to artificially assign weights to the elements for the first time. Since it is a human subjective assignment, errors cannot be avoided, but one should try to listen to expert advice and search for enough information before assigning weights to the elements. The following fuzzy judgment matrix is obtained: (3) $A = [\begin{matrix} a_{11} & a_{12} & \dots & a_{1 n} \\ a_{21} & a_{22} & \dots & a_{2 n} \\ ⋮ & ⋮ & ⋱ & ⋮ \\ a_{m 1} & a_{m 2} & \dots & a_{m n} \end{matrix}]$

Through the establishment of the above judgment matrix, the relationship between each indicator is obtained by using the 1 to 9 scale method, and it is easy to find that the matrix has the following mathematical characteristics: (4) $\begin{matrix} a_{i j} > 0 \\ a_{i j} = \frac{1}{a_{j i}} \\ a_{i j} = 1 \end{matrix}$

From the above properties, it is only necessary to make a judgment about its upper or lower triangular elements. a_ij denotes the importance of comparing elements i and j.

2.4.3

Calculation of weights

Eigenvector w can not be used directly after being calculated, because it will produce a large error and violate the scientific principle of modeling, so it also needs to be preprocessed to make it satisfy $\sum_{i = 1}^{n} W_{i} = 1$ , so that the weight data of each indicator can be obtained accurately. There are many methods to find the eigenvectors, such as geometric mean method, arithmetic mean method, eigenvalue method and so on. Take the geometric mean method to find the eigenvector w as an example:

Step 1: Compute the product of each row of the judgment matrix (M₁, M₂, …, M_i, …, M_n)^T.

Step 2: Calculate the value Y of the nrd power root of M_i.

Step 3: Normalize the n th power root, $W_{i} = Y_{i} / \sum_{i = 1}^{n} Y_{i}, W = {(W_{1}, W_{2}, W_{3}, \dots, W_{n})}^{T}$ is the resulting eigenvector, which is the weight of the element.

Step 4: Calculate the largest eigenvalue: λ_max.

2.4.4

Consistency test

The selection of evaluation indexes is through expert consultation, network search and other methods to the initial selection, so there is a great deal of human subjectivity, this error can not be eliminated, but only constantly reduced, which also affects the calculation of the eigenvalues later. Therefore, after deriving λ_max, it is necessary to do the consistency test, which is to reduce the error and improve the accuracy of the results. The specific test method is as follows:

Calculate the consistency CI: (5) $C I = (λ_{\max} - n) / (n - 1)$

Calculate the consistency ratio CR: (6) $C R = C I / R I$

When CR < 0.1, the academically specified range, it can be judged that the construction of the matrix is consistent with the principle and can be used in practical research. Of course the smaller the CR, the higher the consistency of the judgment matrix in the decision-making goal.

3

Analysis of optimized applications of national governance systems for data science

3.1

Measurement of the discriminatory capacity of indicators for the assessment of governance system optimization

Discriminative ability refers to the extent to which an appraisal indicator is able to distinguish the characteristics of the object to be assessed, reflecting the effectiveness of the appraisal indicator in distinguishing the object to be assessed. An appraisal indicator with good discriminative ability should be effective in distinguishing the differences of appraisal objects, and the discriminative ability can be used as an important basis for evaluating the scientificity of appraisal indicators and screening appraisal indicators.

Screening assessment indicators through the analysis of discriminatory ability, in the study we usually use the coefficient of variation to measure the discriminatory ability of assessment indicators, the coefficient of variation formula is as follows: (7) $\begin{array}{l} X = \frac{1}{n} \sum_{i = 1}^{n} X_{i} \\ S_{i} = \sqrt{\frac{\sum {(X_{i} - X)}^{2}}{n - 1}} \\ V_{i} = \frac{S_{i}}{X} \end{array}$ Where X is the mean and S_i is the standard deviation. If the coefficient of variation is larger, it means that the identification ability of the assessment indicator is stronger. On the contrary, if the coefficient of variation is smaller, it means that the appraisal index is weaker. Since the coefficient of variation has both positive and negative, it is squared, and the indicators are screened and eliminated with the standard of 0.1. The result of the discriminative ability of the evaluation system of comprehensive governance in place A is shown in Figure 2.

The data in the figure show that the squared coefficients of variation of the 29 four-level evaluation indicators designed in this paper are all between 0.5 and 1.Converting the 29 indicators into the corresponding secondary indicators, the average discriminatory power of Digital infrastructure, Agricultural digitization, and Governance digitization is obtained as 0.734, 0.876, and 0.775, respectively.Therefore, the identification ability of the comprehensive governance performance assessment indicator system in place A is relatively good, and can be used for the optimization of the governance system to measure the results.

3.2

Optimization based on fuzzy analysis focuses on the assessment of indicators

Based on the results of the previous study and analysis, in order to rationally allocate the weights of the assessment indicators, this section invites five departmental leaders with rich experience in governance work as experts to make an objective and fair assessment of the importance and degree of influence of the 29 assessment indicators. According to the results of the experts’ evaluation, fuzzy hierarchical analysis was utilized to assign fuzzy scores to the 29 indicators.The 29 assessment indicators are graded comprehensively by considering the actual work of the comprehensive governance in A place. According to the importance and influence of the assessment indicators, the assessment indicators were categorized into four levels: Level I (90-100 points), Level II (80-90 points), Level III (70-80 points), and Level IV (60-70 points), with Level I indicating the highest level of importance and influence, and Level IV indicating the lowest level of importance and influence. The fuzzy scores of the results of the expert assessment are shown in Figure 3, and Table 2 shows the results of the grade division of the assessment indicators.

Table 2.

The grade of the nuclear index is divided

Secondary indicator	Four level index	Average score	Grade division
Digital infrastructure	Internet penetration	80.13	II
	4G coverage	90.588	I
	Cable length	88.82	II
	Smartphone ownership	94.884	I
	Broadband access users	82.628	II
	Computer ownership	92.466
	Investment in scientific research and technology services	93.974	I
	Transportation and other investments	95.524	I
Agricultural digitization	The application rate of information technology in the planting industry	82.946	II
	The application rate of information technology in animal husbandry	80.58	II
	Product network sales rate	78.282	III
	The digital level of agricultural processing equipment	87.206	II
	Product production system management information level	82.296	II
	The level of information of agricultural administrative law enforcement	91.132	I
	The establishment of the rural digitization service organization	90.158	I
	Information service technician	83.888	II
Governance digitization	Number of e-commerce enterprises	88.138	II
	Intelligent tourism revenue	84.604	II
	Digital financial institution	92.31	I
	Library number	89.78	II
	Digital library construction number	82.856	II
	Online number	85.964	II
	Efficiency of Internet services	81.9	II
	Administrative coverage	93.272	I
	Number of digital medical institutions	94.152	I
	The penetration rate of multimedia classroom	93.084	I
	Medical insurance coverage	92.822	I
	Pollution emission detection system coverage	81.13	II
	Access to toilets	91.712	I

This section summarizes the judgment results of five experts, computes the average weighted score for each indicator, and categorizes the importance level of each indicator based on its average score. Combined with the chart, it can be seen that the experts focus on different indicators for the data science governance optimization solution, among which the fourth-level indicator “Product network sales rate” has the lowest average rating of 78.282, with an importance level of III. The significance of these indicators in data science governance optimization is evident by the average scores of the other indicators, which range from 80 to 100.In the opinion of experts, these indicators are more important in optimizing data science governance.

3.3

Impact of governance system optimization on ecological levels

It is known that Site A implemented the optimization strategy of data science governance system in 2019, and this section examines the ecological level of Site A in the last 10 years. The EDA-Malmqust index model is applied to measure the ecological level of the site, with a view to realizing a scientific assessment of the ecological level.The Malmquist index can be decomposed into: (8) $\begin{array}{l} t f p c h = e f f c h \times t e c h c h \\ e f f c h = p e c h \times sec h \end{array}$

It can be seen that the Malmquist (tfpch) index can be decomposed into the product of the composite technical efficiency change index (effch) and the technical progress index (techch), and the composite technical efficiency change index can be further decomposed into the product of the pure technical efficiency change index (pech) and the scale efficiency change index (sech). When tfpch is greater than 1 it indicates that the ecological level is on an upward trend from period t to period t+1 and that efficiency has increased. The index is equal to 1 indicating that there is no change in the ecological level from period t to period t+1. When the index is less than 1, it means that the ecological level has a decreasing trend from period t to period t+1, and the efficiency has decreased.

Figure 4 shows the trend of ecological level change in place A in the last ten years. It can be clearly understood from the figure that before the implementation of the governance system optimization strategy, the ecological level tfpch of place A was less than 1 in all the years except 2015~2016, which showed a decreasing trend. Subsequently, after 2019 know 2023, the tfpch is greater than 1, and the ecological level begins to rise gradually. It shows that the optimization of the national governance system promotes the scientific and standardization of ecological environmental governance.

3.4

Analysis of residents’ satisfaction questionnaire before and after optimization

Based on the research direction combined with the actual situation of the enterprise, this paper designs and prepares a relatively comprehensive questionnaire on the data science governance strategy of place A. A questionnaire is given to the residents of place A to understand in detail the opinions and practices of the residents of place A on the degree of digitization of the governance system before optimization and after optimization, and to analyze the problems.

The main module of the questionnaire survey is the question design for the change of satisfaction with the digitization and optimization of the governance system in place A. The questions are designed from the evaluation indicators above, and 10 questions are set for each of the three secondary indicators of Digital infrastructure, Agricultural digitization, and Governance digitization, taking into account the corresponding fourth-level indicators under them. Ten questions were set up, with the corresponding fourth-level indicators under them.The questions are all multiple-choice questions, and the options are divided into 5 levels: “Very Dissatisfied”, “Dissatisfied”, “Average”, “Satisfied”, and “Very Satisfied”.The respondents selected the corresponding options in the light of their own understanding and daily work, after which the results were summarized to analyze the problems in the implementation of the optimization strategy of the data science governance system in place A.

In order to ensure the independence of the questionnaire filling process and the confidentiality of the results, this time the questionnaire was issued and recovered in an online way through the “Questionnaire Star” small program, a total of 200 questionnaires were issued in this round, and 200 questionnaires were recovered, with an effective recovery rate of 100%. Figures 5 and 6 show the satisfaction survey of the digitization degree of the governance system in place A before and after the optimization of the governance system, respectively.

The data in the figure shows that there was a significant change in the satisfaction of residents of place A with the local digitization before and after optimizing the governance system. Before optimization, the average satisfaction levels of Digital infrastructure, Agricultural digitization, and Governance digitization are 3.313, 3.475, and 3.017, respectively.With the implementation of the optimization strategy of the digital governance system, the degree of digitization in place A has qualitatively improvement, which not only improves the overall effectiveness of national governance, but also provides a convenient public service system for local residents. This has led to an overall increase in residents’ satisfaction, as shown in Figure 6, the average ratings of the optimized Digital infrastructure, Agricultural digitization, and Governance digitization are in the range of 4.085~4.393.

4

Conclusion

This paper assesses the effectiveness of data-driven national governance systems from a Marxist perspective.A variety of research methods were employed to gain knowledge about the governance system in A.Through the evaluation of experts on the establishment of indicators, combined with the fuzzy hierarchical analysis method, the focus items of this system optimization are found.The research results have been obtained as follows:

The identification ability coefficient of the evaluation index system established in this paper is between 0.5 and 1, which can be used for the evaluation of optimization strategies.

Except for “Product network sales rate”, all the other indicators in this paper have an importance level above II.

The optimization of the governance system based on data science can improve the level of ecological environmental governance, and the tfpch index of the ecological level of place A is greater than 1 from 2019 to 2023.

In addition, the data science optimization strategy improves the overall satisfaction of the residents, and the average satisfaction of the optimized Digital infrastructure, Agricultural digitization, and Governance digitization is distributed in the range of 4.085~4.393.

Fund Projects:

1) Anhui Province’s Comprehensive Reform and Ideological and Political Ability Enhancement Plan for “Three pronged Education” in Universities: Construction Project for Young Backbone Teams in Ideological and Political Work in Universities (sztsjh-2023-8-15).

2) Anhui Wenda Information Engineering College “Four Histories” Learning and Education Practice Base (sztsjh-2024-10-5).

3) Teacher Instructor Studio for Collaborative Education of Party and Youth League Classes (sztsjh-2023-4-15).

Lingua:: Inglese

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Research on the Optimization of National Governance System Based on Data Science under the Perspective of Marxism

Lijun Tang

Pubblicato online: 21 mar 2025

Ricevuto: 15 ott 2024

Accettato: 09 feb 2025

DOI: https://doi.org/10.2478/amns-2025-0665

Parole chiaveData Science, Governance Optimization Strategies, Evaluation Indicator Construction, Fuzzy Hierarchical Analysis

© 2025 Lijun Tang, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Data Science, Governance Optimization Strategies, Evaluation Indicator Construction, Fuzzy Hierarchical Analysis