Social recommendation aims to use users’ social attributes to recommend potential items of interest， which effectively alleviates the problems of data sparsity and cold start. However， the existing social recommendation algorithms mainly focus on studying a single social relationship， and social attributes are difficult to fully participate in calculations， so that there are problems of failure to fully explore social heterogeneous relationships and poor quality of node feature representation. Therefore， an enhanced GNN model for social recommendation with Heterogeneous Relationship （HR-GNN） was proposed. In HR-GNN， Graph Convolutional Network （GCN） was used to aggregate user and item node information to generate query embeddings for node information query； the social heterogeneity relationships were explored by neighbor sampling strategy that combines sampling probabilities with consistency scores among neighbor nodes； and the node information was aggregated by self-attention mechanism to improve the quality of user and item feature representation. Experimental results on two real-world datasets demonstrate that in comparison with baseline algorithms， the proposed algorithm has significant improvements in both Mean Absolute Error （MAE） and Root Mean Square Error （RMSE）， and they are reduced by at least 1.80% and 1.35% on Ciao dataset and at least 2.80% and 3.18% on Epinions dataset， verifying the effectiveness of HR-GNN model.