Construction and Optimization of an Intelligent Recommendation System for Ideological-Political Education in Business Administration Courses: Integrating K-Means Clustering and NLP Analysis

Under the background of the deepening teaching reform of “curriculum ideological and political affairs” in business administration courses, how to realize the organic integration of ideological and political elements and professional knowledge has become an important topic to improve the effectiveness of classroom. However, due to the differences in students 'cognitive basis, interest tendency and value expression mode, the traditional content push is often difficult to effectively reach students' internal needs. This study tries to build a set of intelligent recommendation system based on K-Means clustering and natural language processing (NLP) technology, so as to support the accurate embedding and personalized presentation of the course ideological and political content in the business administration teaching. Based on the student behavior data accumulated in the learning platform, the system extracts typical learning tracks and builds multi-dimensional user portrait; at the same time, it conducts the semantic annotation and content clustering of teaching resources with the help of BERT semantic model, so as to realize the ideological and political content push with higher matching degree. The teaching experiment was carried out among 280 undergraduates in a university. By setting up the experimental group and the control group, the use effect of the system was observed and compared. The results showed that the students in the experimental group were more active in resource access activity, test score, content identity, and showed obvious typed characteristics in the feedback behavior recommended by the system. What is more noteworthy is that some students show strong value resonance and active expression tendency in the interaction process of ideological and political content of the course, indicating that technical intervention can activate their ideological response to a certain extent. This study not only verifies the practical value of intelligent recommendation in improving teaching suitability, but also emphasizes the basic position that educational technology should serve “guidance” rather than “discipline”. In the future, the stability of the system and the teaching adaptability will be further optimized around the direction of emotion recognition, cold start user modeling and multi-modal recommendation mechanism.