Research on the optimisation of music education curriculum content and implementation path based on big data analysis

In the era of big data, the rich content of music curricula has brought about the problem of “curriculum content overload.” It is crucial to improve the quality of music education by effectively guiding learners to find the content they are interested in among the vast array of course resources. In this paper, we use big data analytics to investigate the correlation between students’ learning behaviors and courses and to design a personalized course recommendation system for students. Based on the Hadoop big data distributed computing framework, we incorporate the Node2vec algorithm, combine it with machine learning to train learning behavior data, and propose a course content recommendation method based on Node2vec. Analysis of the data shows that the performance of the Node2vec algorithm on Pre@20, Recall@20, NDCG@20, MRR, and AUC evaluation metrics improves by 10.23%, 5.96%, 14.89%, 9.05%, and 4.14% compared to the best performance of the eight baseline models on each evaluation metric. It validates the effectiveness of this paper’s method in recommending course content, as well as its practicality. It also proposes an effective implementation path for enhancing the content of music education courses.