Currently, music education in higher education institutions relies solely on traditional music composition textbooks to fulfill teaching objectives, which no longer meets the demands for music talent. Digital music technology, as a revolutionary innovation in music technology, has transformed people’s understanding of music production. This study first leverages AIGC and multimodal large-scale model technology to assist music classroom instruction, and proposes a personalized music learning path recommendation strategy based on ant colony algorithms. By aligning with students’ learning needs, it precisely recommends scientifically sound learning paths to enhance learning efficiency. Experimental results show that the recommendation algorithm achieves high prediction accuracy, aligning with students’ needs. Additionally, the recommendation system’s surprise factor and real-time performance meet the requirements of resource recommendation systems, making it suitable for implementing teaching resource recommendations. Finally, based on practical analysis of the application of learning path recommendations in music education, results indicate that after practical teaching, the experimental group’s aesthetic perception, artistic expression, cultural understanding, and creative practice levels were significantly higher than those of the control group.