HINs (heterogeneous information networks) have rich semantic information, which are widely used in recommendation tasks. Traditional recommendation methods for heterogeneous information networks ignore the heterogeneity of association relationships and the interaction between different association types. In this study, a recommendation model based on multi-view embedding fusion is proposed, which can effectively guarantee the accuracy of recommendation by mining the deep potential features of networks from the view of homogenous association and heterogeneous association respectively. For the view of homogenous association, a graph convolutional network (GCN)-based embedding fusion method is proposed. The local fusion of node embeddings is realized through the lightweight convolution of neighborhood information under the action of homogeneous associations. For the view of heterogeneous association, an attention-based embedding fusion method is proposed, which uses attention mechanism to distinguish the influence of different association types on node embedding, and realizes the global fusion of node embedding. The feasibility and effectiveness of the key technology proposed in this study are verified by experiments.