We propose a local stereo matching algorithm that integrates tree segmentation with side window technology to enhance disparity estimation accuracy at edge regions, which is a common issue in existing local stereo matching algorithms. Unlike the traditional fixed window aggregation strategy, the proposed algorithm utilizes a cost aggregation strategy based on side window technology. This approach adaptively selects the optimal side window for cost aggregation, substantially improving the disparity accuracy in edge regions. Moreover, tree segmentation technology is employed during the disparity refinement stage to propagate reliable pixel points through circular searches, thereby enhancing disparity accuracy across edges and complex textured areas. Experimental results from the Middlebury dataset demonstrate that proposed algorithm achieves high accuracy and efficiency in disparity calculation, particularly excelling in challenging areas such as image edges and complex textures.