To address the issues of poor robustness and large model parameters in existing stereo matching algorithms in areas such as weak texture images, the PSMNet stereo matching method is improved by using an atrous spatial convolutional pooling pyramid structure (ASPP) to extract spatial feature information of images at different scales. Subsequently, a channel attention mechanism is introduced to assign corresponding weights to feature information at different scales. The above information is integrated to construct a matching cost volume, an hourglass shaped encoding and decoding network is used to standardize it, and determine the correspondence between feature points in various disparity situations. Finally, the linear regression is used to obtain the corresponding disparity map. Compared with PSMNet, the error rates of this study in the SceneFlow and KITTI2015 datasets are reduced by 14.6% and 11.1% respectively, and the computational complexity is reduced by 55%. Compared with traditional algorithms, it can improve the accuracy of disparity maps and enhance the quality of 3D reconstructed point cloud data.