Analysis of Psychological Shaping Function of Music Education under the Background of Artificial Intelligence

In order to solve the problem of integrating intelligent technology into music teaching, this paper puts forward the methods of using intelligent technology to optimize the music teaching system; to enhance the effectiveness of music psychological guidance, music intelligent creation, the development of Yi Guzheng platform, the integration of online sparring technology, and the rational use of Mu class platform, and to ensure the construction of loop curriculum system with "intelligent piano." RBF algorithm has strong data processing ability, which can ensure the operation quality of music score, music score and performance learning modules, and effectively strengthen the training effect of music psychological function. The artificial intelligence platform is more progressive in evaluation. It can use emotion to evaluate courses, grasp the course direction in advance, ensure the construction quality of psychological function, and improve the effect of music teaching; take 100 people as the basis of students, and measure the time proportion of students' music learning in the intelligent system. c σ refers to the learning situation of music courses within the learning time X of students with excellent psychological quality of music. This result is parallel. Y represents the best score of music output by each algorithm. 2024 music intelligent platforms have collected various course resources with difficulty coefficients of 1 to 5, and the course resources of pictures and videos are sufficient. It can provide students with comprehensive music psychological education and give full play to the teaching advantages of intelligent technology. It can be seen from the data: the operation of music score, music score and performance module viewed in March 2021, with high transportation times and resource download times, indicating that the operation is in good condition; The error correction accuracy of the system is greater than 99%, indicating that the system has strong error correction ability.