Application of deep learning in improving music literacy of college students

This paper proposes innovative solutions based on deep learning for the dilemma of music literacy training for college students in the digital age. This paper studies the key role of focused BiLSTM in music recommendation system, and constructs a recommendation framework integrating temporal modelling and multiobjective optimization to achieve deep matching between user interest evolution law and music artistic characteristics. The experiment uses 36-month behavioural logs and multidimensional music features to verify the model's significant advantages in recommendation accuracy, style coverage and aesthetic guidance ability. The results show that this recommendation method not only ensures user experience, but also promotes systematic construction of music cognitive system through dynamic balance recommendation strategy, and provides quantifiable technical realization path for contemporary music quality education.