Graph neural networks (GNNs) have shown great power in graph-related tasks, while recent studies have shown that GNNs are vulnerable to adversarial attacks. Therefore, developing a robust GNN framework has become a popular research topic. Current defense methods based on structure purification or robust networks are usually limited to feature information and single views, which tend to ignore critical information. To address these challenges, we conduct an in-depth study on local and global information on graphs and propose Multi-view Contrastive Learning for Graph Adversarial Defense (COLA) to improve the robustness of the model. On the one hand, we propose to use edge directionality and graph diffusion to generate two augmented views based on the structure, features, and supervised information of the graph. On the other hand, we use multi-view contrastive learning to encode local and global information by constructing different contrast paths to obtain reliable node representations. We validate the effectiveness of COLA on seven benchmark datasets, including four homophilic graphs and three heterophilic graphs. The results show that COLA successfully resists various attacks and outperforms the state-of-the-art baselines.