Music has an increasing impact on people’s daily lives, and a sterling music recommendation algorithm can help users find their habitual music accurately. Recent research on music recommendation directly recommends the same type of music according to the specific music in the user’s historical favorite list. However, users’ behavior towards a certain cannot reflect the preference for this type of music and possibly provides music the listener dislikes. A recommendation model, MCTA, based on “User-Point-Music” structure is proposed. By clustering users’ historical behavior, different interest points are obtained to further recommend high-quality music under interest points. Furthermore, users’ interest points will decay over time. Combined with the number of music corresponding to each interest point and the liking degree of each music, a multi-interest point attenuation model is constructed. Based on the real data after desensitization and encoding, including 100,000 users and 12,028 pieces of music, a series of experimental results show that the effect of the proposed MCTA model has improved by seven percentage points in terms of accuracy compared with existing works. It came to the conclusion that the multi-interest point attenuation model can more accurately simulate the actual music consumption behavior of users and recommend music better.