The construction of student-centered artificial intelligence online music learning platform based on deep learning

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. Especially when the embedding dimension is 64, the Area Under the Curve (AUC) of the model is the highest, and the performance tends to be stable when the sequence length is 20, which is 0.872. Further recall experiments show that with the increase of the number of recommendations, the highest recall rate of CRM-SLIE is 0.364, which is better than other comparative models and can better meet the learning needs of students. 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.