The analysis of optimization in music aesthetic education under artificial intelligence - Peng, Yixuan (Scientific reports, 2025)
In the artificial intelligence (AI) domain, effectively integrating deep learning (DL) technology with the content, teaching methodologies, and learning processes of music aesthetic education remains a subject worthy of in-depth exploration and discussion. The aim is to meet to the music aesthetic needs of students across different age groups and levels of musical literacy. In this paper, the concepts of AI and DL algorithm are first introduced, and their algorithm principles and application status are understood. Then, they are integrated into the application of music aesthetic education, and the algorithm principles and running codes are designed. Finally, experiments are carried out to verify the accuracy of music emotion recognition based on DL algorithm in AI environment to verify the effectiveness of music aesthetic education method based on DL. The results show that the algorithm proposed in this paper has higher accuracy, which combines the advantages of AI and DL algorithm, and obtains higher recognition accuracy. It provides more possibilities for future music aesthetic teaching activities. This paper is dedicated to investigating the feasibility and approach to optimizing the method of music aesthetic education through DL. Its objective is to chart a new developmental direction and practical pathway for music aesthetic education in the era of AI.