As a heterogeneous graph representation learning method, heterogeneous graph neural networks can effectively extract complex structural and semantic information from heterogeneous graphs, and have achieved excellent performance in node classification and connection prediction tasks, which provides strong support for the representation and analysis of knowledge graphs. Due to the existence of some noise interaction or missing interaction in the heterogeneous graph, the heterogeneous graph neural network incorporates erroneous neighbor features when nodes are aggregated and updated, thus affecting the overall performance of the model. In order to solve the above problems, this study proposes a heterogeneous graph structure learning model enhanced by multi-view contrastive. Firstly, the semantic information in the heterogeneous graph is maintained by using the meta path, and the similarity graph is generated by calculating the feature similarity between the nodes under each meta-path, which is fused with the meta-path graph to optimize the graph structure. By comparing the similarity graph and meta-path graph as different views, the graph structure is optimized without the supervision information, and the dependence on the supervision signal is eliminated. Finally, in order to solve the problem that the learning ability of neural network model is insufficient at the initial stage of training and there are often error interactions in the generated graph structure, this study designs a progressive graph structure fusion method. Through incremental weighted addition of meta-path graph and similarity graph, the weight of similarity graph is changed in the fusion process, it not only prevents erroneous interactions from being introduced in the initial stage of training, but also achieves the purpose of using the interaction in similarity graph to suppress interference interaction or complete missing interaction, thus the structure of heterogeneous graph is optimized. The node classification and node clustering are selected as the verification tasks of graph structure learning. The experimental results on four real heterogeneous graph datasets prove that the heterogeneous graph structure learning method proposed in this study is feasible and effective. Compared with the optimal comparison model, the performance of proposed model has been significantly improved under two evaluation metrics.