The GCN-based collaborative filtering model achieves good performance in the recommendation field, but existing graph collaborative filtering learning methods usually do not distinguish the interaction relationship between users and items, which makes it difficult to mine the underlying intentions of user behavior. To address these issues, a decoupling graph contrastive learning recommendation model is proposed. Firstly, users and items are embedded into independent spaces to decouple their intentions. Secondly, during the graph propagation phase, potential semantic neighbors are discovered based on the intention features of users and items. The representation learning of structural and semantic neighbors is decoupled based on intent similarity, generating complete high-level representations for users and items. In the contrastive learning phase, nodes are randomly perturbed to create contrastive views, and contrastive learning tasks are constructed for both structural and semantic aspects. Finally, a multi-task strategy jointly optimizes the supervised task and the contrastive learning task. Experimental results on Yelp2018 and Amazon-Book datasets show that the proposed model outperforms the optimal baseline model NCL. It demonstrates improvements of 7.54% and 5.65% in Recall@20, and 8.57% and 6.28% in NDCG@20 on the two datasets, respectively.