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<p>Under the new media environment, exploring how Shaanxi red cultural resources can be integrated into party building education in colleges and universities significantly improves students’ political literacy and cultural cognition. The study adopts new media technology, combines K-means clustering algorithm and fuzzy cluster analysis method to extract students’ personalized characteristics, and uses them to optimize the content of party building education. The experimental data show that the intra-class average error of the K-means algorithm in the classification of excellent students is 2.56, while the overall average intra-class mistake of the fuzzy clustering algorithm is 10.28, indicating that the latter is more scientific and accurate in party assessment. Through the research and analysis of the student groups in 30 universities, it was found that 80% of the students believed that the universities paid attention to the development of intelligent party building, and 0.9198 of the students thought that the red cultural resources in Shaanxi could meet the needs of party building education. The study’s conclusion shows that combining technical means in the new media environment and Shaanxi red cultural resources can effectively improve the effect of party building education work in colleges and universities, and enhance students’ political literacy and cultural cognition.</p>
</abstract>

Under the new media environment, exploring how Shaanxi red cultural resources can be integrated into party building education in colleges and universities significantly improves students’ political literacy and cultural cognition. The study adopts new media technology, combines K-means clustering algorithm and fuzzy cluster analysis method to extract students’ personalized characteristics, and uses them to optimize the content of party building education. The experimental data show that the intra-class average error of the K-means algorithm in the classification of excellent students is 2.56, while the overall average intra-class mistake of the fuzzy clustering algorithm is 10.28, indicating that the latter is more scientific and accurate in party assessment. Through the research and analysis of the student groups in 30 universities, it was found that 80% of the students believed that the universities paid attention to the development of intelligent party building, and 0.9198 of the students thought that the red cultural resources in Shaanxi could meet the needs of party building education. The study’s conclusion shows that combining technical means in the new media environment and Shaanxi red cultural resources can effectively improve the effect of party building education work in colleges and universities, and enhance students’ political literacy and cultural cognition.

Shaanxi Red Cultural Resources into Party Building Education in Colleges and Universities under New Media Environment

Applied Mathematics and Nonlinear Sciences

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

Under the new media environment, exploring how Shaanxi red cultural resources can be integrated into party building education in colleges and universities...

Shaanxi Red Cultural Resources into Party Building Education in Colleges and Universities under New Media Environment

Published Online: Feb 26, 2024

Page range: -

Received: Jan 15, 2024

Accepted: Jan 24, 2024

DOI: https://doi.org/10.2478/amns-2024-0646

Keywords
K-means, Fuzzy cluster, New media technology, Party building

© 2023 Zhaohui Duan, published by Sciendo

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

Shaanxi Red Cultural Resources into Party Building Education in Colleges and Universities under New Media Environment

Published Online: Feb 26, 2024

Page range: -

Received: Jan 15, 2024

Accepted: Jan 24, 2024

DOI: https://doi.org/10.2478/amns-2024-0646

KeywordsK-means, Fuzzy cluster, New media technology, Party building

© 2023 Zhaohui Duan, published by Sciendo

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

Keywords
K-means, Fuzzy cluster, New media technology, Party building