Automating the task of music genre classification offers opportunities to enhance user experiences, streamline music management processes, and unlock insights into the rich and diverse world of music. In this paper, an improved ViT model is proposed to extract more comprehensive music genre features from Mel spectrograms by leveraging the strengths of both convolutional neural networks and Transformers. Also, the paper incorporates a channel attention mechanism by amplifying differences between channels within the Mel spectrograms of individual music genres, thereby facilitating more precise classification. Experimental results on the GTZAN dataset show that the proposed model achieves an accuracy of 86.8%, paving the way for more accurate and efficient music genre classification methods compared to earlier approaches.