Multibehavior collaborative filtering recommendations can significantly alleviate data sparsity issues caused by insufficient single-behavior information, enhancing recommendation performance. However, current multibehavior recommendation methods simply concatenate different behavior representations without further exploring the interactive information between behaviors, thus limiting recommendation effectiveness. To address the limitations, we propose the graph contrastive learning for multibehavior recommendation (GCMR) model. First, we use a shared bottom to capture the connections between different behaviors of each user. Then, we introduce a GCN-based multibehavior contrastive learning approach that employs cross-layer and cross-behavior contrastive learning to capture intrabehavior and cross-behavior network interaction information, which enhances user and item representations. Additionally, we propose a multibehavior feature fusion strategy that integrates user representations (and item representations) to fully exploit latent information of different behaviors and improve network representation performance. Extensive experiments on three open-source datasets demonstrate that the GCMR model outperforms the state-of-the-art, especially on the Tmall dataset, where GCMR achieved an improvement of 19.10% in HR@10 and 16.50% in NDCG@10 over the best baseline.