Existing multi-view attributed graph clustering methods usually learn consistent information and complementary information in a unified representation of multiple views. However, not only will the specific information of the original views be lost under the method of learning after fusion, but also the consistency and complementarity are difficult to balance under the unified representation. To retain the original information of each view, this study adopts the method of learning first and then fusing. Firstly, the shared representation and specific representation of each view are learned separately before fusion, and the consistent information and complementary information of multiple views are learned more fine-grained. A multi-view attributed graph clustering model based on shared and specific representation (MSAGC) is constructed. Specifically, the primary representation of each view is obtained by a multi-view graph encoder, and then the shared information and specific information of each view are obtained. Then the consistent information of multiple views is learned by aligning the view shared information, the complementary information of multiple views is utilized by combining the view specific information, and the redundant information is processed through the difference constraint. After that, the topological structure and attribute feature matrix of the multi-view decoder reconstruction graph are trained. Finally, the additional self-supervised clustering module makes the learning and clustering tasks of graph representation tend to be consistent. The effectiveness of MSAGC is well verified on real multi-view attributed graph datasets.