The robustness of graph classification models plays an essential role in providing highly reliable applications. Previous studies along this line primarily focus on seeking the stability of the model in terms of overall data metrics (e.g., accuracy) when facing data perturbations, such as removing edges. Empirically, we find that these graph classification models also suffer from semantic bias and confidence collapse issues, which substantially hinder their applicability in real-world scenarios. To address these issues, we present MGRL, a multi-view representation learning model for graph classification tasks that achieves robust results. Firstly, we proposes an instance-view consistency representation learning method, which utilizes multi-granularity contrastive learning technique to perform semantic constraints on instance representations at both the node and graph levels, thus alleviating the semantic bias issue. Secondly, we proposes a class-view discriminative representation learning method, which employs the prototype-driven class distance optimization technique to adjust intra- and inter-class distances, thereby mitigating the confidence collapse issue.Finally, extensive experiments and visualizations on eight benchmark dataset demonstrate the effectiveness of MGRL.