Aiming at the student-centered online music learning platform, this study proposes a Course Recommendation Model for Student Learning Interest Evolution (CRM-SLIE) to improve the accuracy and adaptability of the platform’s course recommendation. This model combines attention mechanism and Gated Recurrent Unit (GRU), and introduces project crossing module, which can effectively capture students’ interest changes and second-order characteristic interaction among courses. The experimental results show that the CRM-SLIE model has excellent performance under different embedding dimensions and the length of student behavior sequence. In addition, the results of ablation experiments show that the position coding and the way of item crossing have a significant impact on the model performance, and the combination of inner product and Hadamard product is particularly effective in capturing the complex relationship among courses. The research shows that CRM-SLIE model has strong adaptability, robustness and practical application value in the course recommendation task, and can provide personalized and accurate learning resource recommendation for online music learning platform.
