In order to deal with the problems of quality differences among different views， ambiguous boundary samples and differences in semantic structures among different views， we propose deep multi-view clustering with dual-channel granular computing. A dual-channel feature fusion module is designed to strengthen key representations， where the global average pooling channel captures holistic semantics， and the global max pooling channel focuses on highly discriminative cues. Furthermore， a dual-channel contrast learning strategy is introduced for contrast learning at the sample and local fuzzy granular-ball structure level respectively. Fuzzy granular-ball level contrast learning is divided into intra-granular-ball and cross-view fuzzy granular-ball contrast learning. The former optimizes the clustering boundary by making positive samples inside the granular-ball closer. The latter ensures consistent granular-ball structures are learned across different views. Additionally， this paper introduces a view-adaptive attention weight assignment mechanism that enhances the leading role of high-quality views in clustering. We verify the effectiveness of our method on eight publicly available multi-view datasets. The results show that our method improves clustering accuracy compared to the existing multi-view clustering methods， such as MFLVC， SCMVC， etc.