Multi-Dimensional Analysis of the Nurturing Function of Red Culture into the Path of Ideological Education in Colleges and Universities Using Big Data

Abstract

This paper utilizes distributed computing frameworks and other data processing technologies to achieve intelligent processing of multimodal red cultural resources, providing educational resources for ideological and political education in higher education institutions. By employing a pre-trained BERT model to generate document-word co-occurrence maps, combined with continuous multivariate probability (Dirichlet) distributions to optimize topic representations, and based on an improved BERTopic algorithm, a red cultural topic clustering model is constructed. The results show that when red culture themes are clustered into four categories, the top three keywords in terms of frequency are: ideals and beliefs, patriotic sentiment, and red gene. When the themes are simplified into three categories, the algorithm clustering time is 60.513 seconds, the contour coefficient is 0.264, the CH index is 1267.453, and the content coverage rate is close to 100%. Using the clustered red culture for ideological and political education, students' ideological and political literacy improved by an average of 0.988 points.