Recently, GNN(Graph Neural Network) recommender systems have benefitted from contrastive learning as an auxiliary task of recommendation and have employed data augmentation to overcome the data sparsity problem. However, we find that the training process of contrastive learning is affected by the popularity bias due to the longtail distribution of interaction data, resulting in the inadequate feature training of low-degree nodes. To address this problem, we propose DCLGCF (Debiased Contrastive Learning For Graph Collaborative Filtering). More specifically, we propose two data augmentation methods with respect to popularity reduction and longtail enhancement. In addition, we propose Mixed-InfoNCE, which designs a novel mixed sampling strategy and introduce a new contrastive learning loss function by considering a frequency penalty term, aiming at increasing the contribution of longtail items to the gradient calculation, and enhancing the training of longtail item features. To validate the effectiveness of our proposed DCLGCF, we conduct thorough experiments on four real-world datasets. The results clearly demonstrate that DCLGCF outperforms existing models in terms of recommendation accuracy, and remarkable improvements are achieved especially when recommending longtail items.