Abstract: Graph neural networks (GNNs) learn graph embeddings by iteratively aggregating neighborhood features and have been widely applied in graph data analysis. Existing methods primarily focus on low-order node-edge interactions while overlooking high-order group interaction patterns captured by motifs. This limitation hinders the effective modeling of high-order dependencies in complex networks. Motifs, as frequently occurring functional substructures in networks, can effectively reveal high-order semantic relationships among nodes. The motif co-occurrence view provides a novel perspective for representing such interactions. However, the weak connectivity of motif co-occurrence views restricts message passing in GNNs, thereby impeding the effective propagation of global information. To address this issue, we propose a motif-aware multi-view cooperative clustering optimization algorithm (MMCC), which fully exploits high-order topological information through an adaptive multi-view fusion mechanism while enhancing representation consistency across different views via contrastive learning to alleviate the issue of restricted message passing. Specifically, MMCC first constructs multiple motif co-occurrence views based on different motif structures and employs a semantic attention-based multi-view auto-encoder to dynamically learn the importance of different motif views, achieving adaptive fusion. Then, contrastive learning is introduced to enforce embedding space consistency between the original view and motif co-occurrence views, mitigating the message passing limitations caused by weak connectivity. Finally, by optimizing a KL-divergence-based objective function, MMCC jointly optimizes feature learning and clustering tasks. Experimental results on seven real-world networks demonstrate that MMCC outperforms eight baseline algorithms in clustering accuracy (ACC), normalized mutual information (NMI), F1-score (F1), and adjusted Rand index (ARI), highlighting its effectiveness in high-order network clustering.