Multi-label image classification is a research hotspot in multi-label data classification. The existing multi-label image classification methods only learn the visual representation features of images and ignore the relevant information between image labels and the correspondence between label semantics and image features. In order to solve these problems, a multi-label image classification model based on a multi-head graph attention network and graph model (ML-M-GAT) is proposed. By using label co-occurrence and attribute information, the model builds a graph model, and it employs the multi-head attention mechanism to learn the attention weight of the label. In addition, the model utilizes label weights to fuse label semantic features and image features, so as to integrate label correlation and label semantic information into the multi-label image classification model. In order to verify the effectiveness of the proposed model, experiments are carried out on the public datasets VOC-2007 and COCO-2014, and the experimental results show that the average mean accuracy (mAP) of the ML-M-GAT model on the two datasets is 94% and 82.2%, respectively, which are better than that of CNN-RNN, ResNet101, MLIR, and MIC-FLC models and are 4.2% and 3.9% higher than that of ResNet101 models, respectively. Therefore, the proposed model can improve the performance of multi-label image classification by using image label information.