With the rapid development of information technology, Artificial Intelligence (AI) gradually penetrates into all walks of life, especially in the field of education. As a special form of art education, vocal music teaching has an increasing demand for personalization and precision in teaching. In this paper, a vocal music teaching mode optimization method based on AI algorithm is proposed, and a personalized learning path recommendation model is designed. First, a vocal music knowledge graph is constructed, and knowledge acquisition and extraction is carried out through multimodal data fusion (e.g., audio, sheet music, and singing lyrics, etc.). Then, deep learning algorithms (e.g., CNN, LSTM, and RankNet) are used to achieve dynamic recommendation of learning paths based on learners’ personalized features. The experimental results show that after using the improved path recommendation algorithm, the learning effect of the learner is significantly improved, especially in the mastery of knowledge points related to the learning objectives, which is improved by more than 10%. In addition, the recommended learning paths were highly evaluated by more than 80% of learners through a user satisfaction survey. The study shows that the personalized learning path based on the model can effectively enhance learners’ learning gains, and provides theoretical and practical basis for personalized teaching of vocal music.
