In order to deal with the problems of quality differences among different views, hard clustering methods leading to difficulties in pro-cessing boundary samples, differences in semantic structures among different views, and the fact that most of the existing multi-view clustering methods only perform similarity learning at the sample-level features, ignoring the local structures and multi-granularity in-formation in the views. In this paper, we propose deep multi-view clustering with dual-channel granular computing. This method extracts key features from each view through dual-channel feature fusion and introduces a dual-channel contrast learning strategy 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 dif-ferent views. Addi-tionally, this paper introduces a view-adaptive attention weight assignment mechanism that en-hances the leading role of high-quality views in clustering. We verified the effectiveness of our method on eight publicly available multi-view datasets. The results show that our method improves clustering accuracy compared to existing multi-view clustering methods.