AI Innovative Dissemination of Red Culture in Civic and Political Education in Colleges and Universities--A New Model of Enterprise Operation Participation

Authors

  • Yuqian Sun School of Marxism/ School of General Education, Liaoning University of International Business and Economics, Dalian, Liaoning, 116052, China

DOI:

https://doi.org/10.70917/ijcisim-2026-0102

Keywords:

red culture dissemination; enterprise operation participation; social network analysis; NEDC-GTOPSIS algorithm

Abstract

Based on the social network analysis method, this paper proposes the analysis method of red culture dissemination in Civic Education. NEDC-GTOPSIS node influence evaluation algorithm is designed, combining gray correlation analysis and approximation of ideal solution method to double evaluate the influence of nodes. Taking the interaction data of the red culture parenting topic of a university microblogging platform as a sample, the study explores the dissemination mechanism and optimization path of red Civic and political education under the participation of enterprise operation. It is found that among the top 10 active users, 30% of the nodes are operated by enterprises. The actual spreading influence of nodes in the communication network with enterprise operation participation is generally the highest, and the actual spreading influence of the communication network without enterprise operation participation is generally lower. The set of seed nodes determined by NEDC-GTOPSIS achieved the widest spreading of influence, which is superior to other comparative algorithms, proving the reliability of this paper's algorithm for the measurement of node influence.

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Published

2026-01-12

How to Cite

Yuqian Sun. (2026). AI Innovative Dissemination of Red Culture in Civic and Political Education in Colleges and Universities--A New Model of Enterprise Operation Participation. International Journal of Computer Information Systems and Industrial Management Applications, 18, 17. https://doi.org/10.70917/ijcisim-2026-0102

Issue

Section

Original Articles