With the application of machine learning to the teaching of classical music in the context of five education as the research objective, this study first tested the feature extraction performance and classification of classical music with specific classical music data. Secondly, it explored the changes in the three elements of song aesthetics before and after machine learning five-education music teaching in two academic periods and analyzed the teaching effect it had on the task completion time and rhythmic accuracy of the songs. The use of audio analysis software in terms of task completion time, rhythmic accuracy, and pitch accuracy dimensions produces quantitative teaching effect ratings. The results show that the method in this paper obtains ideal classical music classification and feature extraction results and the ratings of the music feature extraction algorithms are in line with the real level of teachers and students. The experimental group and the control group, after teaching music using machine learning, took a similar time to learn the task, but the difference in rhythmic accuracy was large. The highest IOI value of the experimental group is only 0.07439, and the overall average of the group’s satisfaction with the teaching effect has increased by 0.842. This paper’s method is conducive to the development and innovation of music teaching.