Binocular stereo matching is an important issue in computer vision research. In order to solve the problem of stereo matching at the depth discontinuities, low textured regions and repetitive structures incidental matching error, a stereo matching algorithm based on image segmentation and improved adaptive support weight is proposed. the initial matching cost that combines the color similarity, euclidean distance similarity, user-defined inter color correlation similarity and gradient similarity is defined. The mean shift algorithm segment matching pixel is used at different depth regions in order to refine the matching cost. Meanwhile, in the process of cost aggregation, the new cost aggregation is calculated based on compare transform matching pixel by ranking transform in stereo image pairs in order to solve the influence of brightness and noise difference between the stereo image pairs, so a more accurate disparity result can be acquired. Finally, the algorithm is tested by Middlebury stereo benchmark on the VS2010 software platform, and the results show that it has better performance than other local matching methods, and its robustness is very strong and has higher accurate matching rate.