For traditional multi-label image classification models is difficult to generate image features that are closer to related labels, and the visual correlation between the labels is not used, which leads to problems such as insufficient recognition accuracy. This paper proposes a multi-label image classification algorithm based on spatial attention and graph convolution. The algorithm first uses the graph convolution network to learn the features of the label adjacency graph, and introduces the spatial attention mechanism into the high-level semantic information to recalibrate the target features. Then, the high-level semantic information and the label co-occurrence features extracted by the GCN network are fused in the classifier based on the co-occurrence feature fusion, and finish the prediction. Comparison experiments on two public data sets show that the average accuracy of the algorithm in the article on the MS-COCO data set is 1.1% higher than that of MLGCN, and the amount of parameters is only one-eighth of the original model, greatly reduced its training cost.