Convolutional neural network and recommendation algorithm for the new model of college music education

With the rapid development of the field of artificial intelligence, we expect that AI+ music can produce a new music education model to help efficient college students improve their personal music level. In this paper, we design a convolutional neural network music recommendation system, including a user modeling module, audio feature extraction module recommendation algorithm module, etc., which can model students' music preferences to generate Top recommendations for target users. It is helpful for teachers to timely and dynamically grasp the types of music that students like. Experiments show that the proposed method has certain feasibility and effectiveness. Compared with other traditional music recommendation algorithms, we can make full use of the powerful advantages of deep neural network automatic feature extraction and integrate the historical behavior information of users' interaction with music.