In order to improve the extraction effect of the deep convolutional neural network on music spectrum genre features， a music genre classification algorithm model based on attention spectral-spatial feature， namely DCNN-SSA （Deep Convolutional Neural Network Spectral Spatial Attention）， was proposed. In DCNN-SSA model， the genre features of different music Mel spectrograms were effectively annotated in the spatial domain， and the network structure was changed to improve the feature extraction effect while ensuring the effectiveness of the model， thereby improving the accuracy of music genre classification. Firstly， the original audio signals were Mel-filtered to effectively filter the sound intensity and rhythm change of the music by simulating the filtering operation of the human ear， and the generated Mel spectrograms were cut and input into the network. Then， the model was enhanced in genre feature extraction by deepening the number of network layers， changing the convolution structure and adding spatial attention mechanism. Finally， through multiple batches of training and verification on the dataset， the features of music genres were extracted and learned effectively， and a model that can effectively classify music genres was obtained. Experimental results on GTZAN dataset show that compared with other deep learning models， the music genre classification algorithm based on spatial attention increases the music genre classification accuracy by 5.36 percentage points to 10.44 percentage points and improves model convergence effect.