In recent years, multi-view clustering (MVC) has become a promising approach for analyzing heterogeneous multi-source data. However, during the collection of multi-view data, factors such as environmental interference or sensor failure often lead to the loss of view sample data, resulting in incomplete multi-view clustering (IMVC). Graph contrastive IMVC has demonstrated promising performance as an effective solution, which typically utilizes in-graph instances as positive pairs and out-of-graph instances as negative pairs. However, the construction of positive and negative pairs in this paradigm inevitably leads to graph noise Correspondence (GNC). To this end, we propose a new IMVC framework, namely robust graph contrastive learning (RGCL). Specifically, RGCL first completes the missing data by using a multi-view consistency transfer relationship graph. Then, to mitigate the impact of false negative pairs from graph contrastive, we propose noise-robust graph contrastive learning to mine intra-view consistency accurately. Finally, we present cross-view graph-level alignment to fully exploit the complementary information across different views. Experimental results on the six multi-view datasets demonstrate that our RGCL exhibits superiority and effectiveness compared with 9 state-of-the-art IMVC methods. The source code is available at https://github.com/DYZ163/RGCL.git.