Research on the Integration of Personalized Learning Resources for Vocal Music Education in Music Teaching in Colleges and Universities in Digital Environment

The decentralized and chaotic nature of the vocal music teaching curriculum makes it difficult for students to complete the learning process efficiently and systematically. To address this situation, this paper proposes a vocal music learning path recommendation algorithm based on a fine-grained vocal music knowledge graph. After data cleaning and entity alignment, the learner inputs vocal music learning objectives and knowledge reserves, generates all learning paths based on the relationship between courses in the constructed knowledge graph, and recommends the optimal learning paths for the learner based on the learner’s preference, learner type, and the characteristics of the course itself. To finally verify the effectiveness of the knowledge graph construction method and the results of vocal music learning path recommendation on the dataset. 875 vocal music courses in the dataset are analyzed, of which 624 courses have prerequisite courses and there are 1398 pairs of prerequisite relationships. Randomly selected 100 prerequisite relationships for judgment, the correct rate of this paper’s method is 89%, with excellent results. The learning path of the knowledge point “playing and singing synergy” and its associated knowledge points is {vocal practice->pitch and rhythm->curve structure->vocal technique->breathing and biting->open and closed mouth sounds->pop singing->playing and singing synergy}, and the path recommendation algorithm of this paper meets the design expectation.