Research on Fuzzy Intelligent Classification of Ideological and Political Red Culture Resources in College English Courses

DOI

10.2991/978-2-38476-462-4_7How to use a DOI?

Keywords

red culture resources; fuzzy C-means; clustering; curriculum

Abstract

To address the issues of vague classification and insufficient teaching pertinence in the current integration of ideological and political education with College English courses, this study proposes a fuzzy clustering algorithm integrating semantic enhancement and weight optimization (SEW-FCM). First, a corpus containing 2,175 red culture resources was constructed, and deep semantic features were extracted using the BERT-wwm model. Second, the traditional FCM algorithm was improved by introducing a teaching relevance weight factor to achieve multi-dimensional resource classification. Experiments show that compared to traditional K-means, this method improves accuracy by 23.7%, with a Xie-Beni index of 0.482. Teaching experiments demonstrate that modular teaching based on the classification results increases students’ ideological and political cognition scores by 31.2% (p < 0.01).

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Lilan Chen
PY  - 2025
DA  - 2025/09/12
TI  - Research on Fuzzy Intelligent Classification of Ideological and Political Red Culture Resources in College English Courses
BT  - Proceedings of the 2025 9th International Seminar on Education, Management and Social Sciences (ISEMSS 2025)
PB  - Atlantis Press
SP  - 50
EP  - 59
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-462-4_7
DO  - 10.2991/978-2-38476-462-4_7
ID  - Chen2025
ER  -