The optimization of vocal music teaching by integrating the STEAM concept with the intelligent recommendation system

To improve the personalization and intelligence level of vocal music teaching, this study integrates the Science, Technology, Engineering, Arts, and Mathematics (STEAM) concept into an intelligent recommendation system. It proposes a teaching optimization model based on multimodal learning and sentiment analysis (SA). The study comprehensively applies Neural Collaborative Filtering (NCF) to realize personalized recommendations, Deep Q-Network (DQN) to optimize teaching strategies, and Generative Adversarial Network (GAN) to generate diverse resources. It also combines multimodal fusion and SA to achieve real-time evaluation. The experiment is based on public data sources such as LibriSpeech, YouTube-8 M, Common Voice, and TED-LIUM. The results show that this model outperforms traditional methods in recommendation precision (F1-Score: 0.88), teaching strategy stability (97.24%), resource generation quality (97.91%), and multimodal fusion accuracy (99.79%). The study demonstrates the advantages of the in-depth integration of the STEAM concept and artificial intelligence. At the same time, it provides a practical new path for optimizing and promoting vocal music teaching. However, the real-time synchronization and deep semantic alignment among multimodal features still inevitably have certain limitations due to the computational complexity of existing algorithms and the limitations of model generalization abilities. In the future, a lightweight architecture and adaptive constraint mechanism can be combined to gradually improve the relevant technical paths.