Large collections of music bring new challenges for people to choose the favorite songs. Music genre explicitly defined can help people to solve this problem. However, classifying the music genre automatically is a challenging problem since many genres do not have any special features. This paper presents a new music genre classification method which utilizes hierarchical analysis of the spectrograms features extracted from the audio signals. First support vector machines are used to build the classification tree, then K nearest neighbors are implemented to improve the accuracy of the classification. The GTZAN genre collection music database is used to evaluate the proposed model, and the results show that this model can get comparable results compared with some other existing music genre classification methods.