Research on the construction of teaching resource recommendation system for ideological and political courses based on big data technology

The "Internet + Education" integration challenges ideological - political education course resources with over - growth and imprecise matching. Traditional recommendations lack personalization and have unclear value orientation. This paper presents a big - data - based recommendation system. It uses a closed - loop design with user profiling, resource modeling, intelligent recommendation, and feedback, along with machine learning and NLP. The system's algorithm includes ideological filtering and dynamic weight adjustment. Experiments prove its effectiveness in recommendation accuracy and educational value, supporting high - quality ideological - political education in the new era.