Node feature representation was learned by Graph Convolutional Network （GCN） by deep graph matching models in the stage of node feature extraction. However， GCN was limited by the learning ability for node feature representation， affecting the distinguishability of node features， which causes poor measurement of node similarity， and leads to the loss of model matching accuracy. To solve the problem， a deep graph matching model based on self-attention network was proposed. In the stage of node feature extraction， a new self-attention network was used to learn node features. The principle of the network is improving the feature description of nodes by utilizing spatial encoder to learn the spatial structures of nodes， and using self-attention mechanism to learn the relations among all the nodes. In addition， in order to reduce the loss of accuracy caused by relaxed graph matching problem， the graph matching problem was modelled to an integer linear programming problem. At the same time， structural matching constraints were added to graph matching problem on the basis of node matching， and an efficient combinatorial optimization solver was introduced to calculate the local optimal solution of graph matching problem. Experimental results show that on PASCAL VOC dataset， compared with Permutation loss and Cross-graph Affinity based Graph Matching （PCA-GM）， the proposed model has the average matching precision on 20 classes of images increased by 14.8 percentage points， on Willow Object dataset， the proposed model has the average matching precision on 5 classes of images improved by 7.3 percentage points， and achieves the best results on object matching tasks such as bicycles and plants.