In order to introduce semantic correlation between labels into multi-label image classification model traditional methods such as ML-GCN transform label conditional probability matrix into label co-occurrence matrix by using single threshold value. However it is difficult to sum up all semantic relationships of all labels by using single threshold value. To solve this problem a method of multi-label image classification with fusing powerful semantic correlation MGAN was proposed. By setting multiple thresholds the traditional conditional probability matrix of labels was divided into multiple subgraphs according to different degrees of correlation. Meanwhile in order to improve the performance of multi-label classification image region spatial correlation was also introduced. In addition the traditional ??CNN +GCN method regards the fusion tensor of label and feature as the lack of interpretability of the predicted fraction. To solve this problem MGAN regards the labels and feature?? s fusion tensor as the attention score. Compared with other mainstream multi-label image classification methods on MS-COCO and PASCAL VOC datasets the mAP were 94. 9% and 83. 7% respectively which were 0. 9% and 0. 8% higher than traditional ML-GCN model. And MGAN performed well in both ??Binary and??Re-weighted adjacency matrix mode which verified that the new fusion method can alleviate the influence of graph convolutional neural network?? s ??over smoothing problem on multi-label image classification.