The content and teaching tools of music education continue to expand, but as students’ learning needs and ability levels continue to change, their limitations become increasingly apparent.. The tendency to focus too much on the training of hard quantitative music skills makes some students unable to perform in the practical application of music even though they can sing a few songs of a higher level. This teaching style makes it simple for students to develop inaccurate professional thinking orientations, ignores the development of students’ teaching abilities, separates learning from usage, and makes it challenging to satisfy societal demands. Computer technology and multimedia technology are now increasingly needed in the teaching activities of contemporary music for auxiliary teaching, in order to foster students’ capacity for independent inquiry and study, which is due to the ongoing development of art education and information technology. This paper analyzes the current research status of music intelligent systems in interactive teaching, explores the application of data mining technology combined with RBF (radial basis function) in interactive music teaching, and constructs a neural music intelligent system model to identify music learning links and design an interactive teaching model for autonomous learning. The findings indicated that the music intelligent system not only enhances students’ overall performance in interactive teaching by 9.17% when compared to the traditional teaching mode, but also has a positive supplemental impact on students’ acquisition of musical information. At the same time, it offered a superior method of instruction, one that is useful in real-world situations and significant for the study of music teaching.
