Music Intelligent Learning System Based on Cloud Computing and Improved Ant Colony Algorithm

Music learning plays an important role in cultivating the aesthetic taste and developing creativity of young people. The current traditional music education is unable to provide rich and diverse learning resources, nor can it pay attention to individual differences among young people and achieve personalized teaching. This article conducted extensive research on the development of a music intelligent learning system by combining cloud computing and improved ant colony optimization (ACO). The goal was to improve the quality of music learning for teens and accomplish individualized music learning. This article built a system architecture with three levels: application layer, service layer, and data layer, after first analyzing the design requirements of a music intelligent learning system. Finally, by adding adjustable control parameters for state transition rules, ACO random optimization was achieved, and the cloud computing technology was combined to achieve personalized learning resource recommendation. This article tested the system performance and learning efficiency from two angles to confirm the efficacy of the music intelligent learning system. The results showed that compared to students who used traditional offline courses for music learning, the average score of students who used the music intelligent learning system in this article for music learning increased by 0.38 points. The conclusion indicated that a music intelligent learning system based on cloud computing and improved ACO could help achieve personalized music learning and improve the effectiveness of youth music learning.