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<p>This paper proposes a split hierarchical cluster analysis algorithm to study the management of political education in colleges and universities. The data mining object of a university is selected, and the initial data for the study of ideological and political education and management is obtained by issuing the questionnaire of “quantitative assessment form for counselors”, and the data is optimized by clustering the data with dissimilarity matrices. Based on completing the pre-processing of the research data, the combination of statistical analysis and simulation analysis is used to analyze the example of the management of college civic education in the context of digital intelligence. The results show that when K is equal to 2, both clusters have similar thickness and size, so it is more appropriate to select K=2 to cluster analysis of each crisis group of ideological and political education management, indicating that the split-level cluster analysis algorithm helps to promote the interconnection of the ideological and political education management work. This study can improve the level and overall quality of college students’ ideological and political education in an all-round way, create a high-level management mode of ideological and political education and promote the sustainable development of ideological and political education in colleges and universities.</p>
</abstract>

This paper proposes a split hierarchical cluster analysis algorithm to study the management of political education in colleges and universities. The data mining object of a university is selected, and the initial data for the study of ideological and political education and management is obtained by issuing the questionnaire of “quantitative assessment form for counselors”, and the data is optimized by clustering the data with dissimilarity matrices. Based on completing the pre-processing of the research data, the combination of statistical analysis and simulation analysis is used to analyze the example of the management of college civic education in the context of digital intelligence. The results show that when K is equal to 2, both clusters have similar thickness and size, so it is more appropriate to select K=2 to cluster analysis of each crisis group of ideological and political education management, indicating that the split-level cluster analysis algorithm helps to promote the interconnection of the ideological and political education management work. This study can improve the level and overall quality of college students’ ideological and political education in an all-round way, create a high-level management mode of ideological and political education and promote the sustainable development of ideological and political education in colleges and universities.

Research on the Sustainable Development and Operation Mechanism Construction of Civic Education Management Path in Colleges and Universities in the Context of Digital Intelligence

Publicity Department of CPC Committee of Chaohu University

Center for International Education, Philippine Christian University

Applied Mathematics and Nonlinear Sciences

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

This paper proposes a split hierarchical cluster analysis algorithm to study the management of political education in colleges and universities. The data...

Research on the Sustainable Development and Operation Mechanism Construction of Civic Education Management Path in Colleges and Universities in the Context of Digital Intelligence

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 02. Jan. 2024

Akzeptiert: 10. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0317

Schlüsselwörter
Dissimilarity matrix, Split-level, Cluster analysis algorithm, Data mining, Civic education management

© 2024 Chao Guo, published by Sciendo

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

Research on the Sustainable Development and Operation Mechanism Construction of Civic Education Management Path in Colleges and Universities in the Context of Digital Intelligence

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 02. Jan. 2024

Akzeptiert: 10. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0317

SchlüsselwörterDissimilarity matrix, Split-level, Cluster analysis algorithm, Data mining, Civic education management

© 2024 Chao Guo, published by Sciendo

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

Schlüsselwörter
Dissimilarity matrix, Split-level, Cluster analysis algorithm, Data mining, Civic education management