<p>Although contrastive learning-based multi-view clustering has advanced recently, it still struggles to capture high-order structural relations and to balance global representations with local neighborhood preservation. To address these problems, we propose a novel multi-view clustering method: Global-Relationship-Aware Multi-View Clustering via Random Walk with Restart (GMCR). Specifically, Random Walk with Restart (RWR) is applied at both the feature and cluster assignment levels to capture global relationships from multiple perspectives while preserving local neighborhood structures. Furthermore, we introduce a structure-guided dual contrastive learning mechanism. It aligns features and cluster assignments using global relational information, thereby enhancing the similarity of structurally correlated samples. In addition, the proposed method is a flexible representation learning module, which is capable of being integrated into the incomplete multi-view clustering task. Extensive experiments verify the effectiveness of the presented method on both incomplete and complete datasets.</p>