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This paper discusses the implementation of informatization strategies in music teaching in colleges and universities, mainly through constructing an online teaching platform, using Bayesian nonparametric models and extreme gradient boosting algorithms to optimize the teaching process by classifying music and recommender systems. The study collected students’ specific information and learning styles, and found that most students solved problems by searching for tutorial books and consulting their classmates, and less often used online resources. The online teaching platform generated feedback reports based on the number of interactions, engagement and attendance. Classroom activities focused mainly on interaction and discussion in the first 15 minutes, while whole-class quizzes were conducted in the 18 to 30 and 40 to 50 minute segments. Finally, the teaching practice was evaluated through a questionnaire, and the results showed that the p-values of the five dimensions of aesthetic perception, artistic performance, cultural understanding, interactive learning and interest inquiry were significantly less than 0.001, indicating that the implementation of informative teaching strategies enormously helped to enhance students’ learning outcomes.

The study uses the MKR (Multi-task Knowledge-aware Recommendation) model, which integrates knowledge graphs and multilayer perceptron networks, and focuses on mining users’ high-level music preferences from music information and user behaviors. The study first introduces the three core parts of the MKR model: the recommendation task module, the knowledge learning module, and the cross-compression unit. Then, by optimizing the MKR model, the A-MKR (Attention-based MKR) model is proposed, which introduces the attention mechanism to further improve the model performance. In the application of music teaching, the study explores the keyword analysis of music education, digital teaching methods and their effect evaluation. The experimental results show that compared with traditional teaching, the digital teaching model performs better in both field assessment and ability growth, and the error rate is significantly reduced. For example, the digital group scored 1.3 and 9 points higher than the traditional group in field assessment and ability growth, respectively, and the error rate was reduced by 5.8%. This study proves the effectiveness and practical value of knowledge mapping based on the MKR model in the digital transformation of music teaching in colleges and universities. It provides new perspectives and methods for future music teaching.