Music education informatization system can promote music teaching; in addition, due to the characteristics of music disciplines such as the audiovisual nature of music, the influence of informatization on music teaching is self-evident. With the rapid development of the human ability to obtain information, machine learning algorithms have been widely used in various fields of scientific research and engineering, involving chemical production statistical process control, archeology text recognition, social and criminal investigation field fingerprint and image recognition, and genomic information research in the field of biomedicine. In order to correctly evaluate the music education information system based on machine learning, through the comparison of four models, it is concluded that the construction of the GBDT model is optimal.

To better realize music education and improve the accuracy of music recommendation, a multidimensional analysis method of the music education system based on multi-intelligent recommendation is proposed. In the process of using this method to recommend music to users, the music characteristics are extracted and the music data are obtained from MIDI music, and three collaborative filtering algorithms are jointly used, namely user-based, content-based, and model-based collaborative filtering algorithms. The simulation results show that the proposed method can provide users with an intelligent music recommendation scheme according to the user’s basic information and operation information. Compared with a single user-based recommendation or content-based, model-based recommendation, the proposed method has a certain degree of novelty and accuracy. Here, the recommendation accuracy rate can reach 94.8%, which is higher than the other two recommendation algorithms, showing certain advantages.