Abstract
People can learn, understand, and use music using the solfeggio method. People involved in professional music training have given it importance because it is a fundamental discipline of enlightenment music and a necessary training method and technical theory course to enter the professional level. This article presents an in-depth analysis of the music curriculum model based on data mining technology. Additionally, a music style classification method based on FP Growth association rules mining is developed, which decreases the number of frequent element sets needed and speeds up database scanning. To better match the characteristics of music, a multirepetition structure model is used, and the MFCC (Mel-frequency cepstral coefficients) function is used to set the repetition structure model. The results demonstrate that the music style classification method put forth in this article has clear advantages in terms of efficiency: when the minimum support is 0.2 percent, this method's execution time is about 33 percent compared to the other methods. The outcomes of the experiment suggest that this method can more accurately capture the repeatability of music and enhance separation performance to some extent.