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Given that traditional depth estimation methods have problems such as insufficient feature extraction, incomplete image information acquisition, and limitations on local information or specific types of feature extraction in high-resolution images, a transformer stereo matching network for global features is proposed. The network adopts an end-to-end architecture of encoder decoder, using a multi head attention mechanism to allow models to focus on different features in different sub-spaces, thereby improving modeling capabilities. The model combines self attention mechanism and feature reconstruction window to improve feature representation ability, compensate for local feature deficiencies, reduce computational burden, and effectively address the high computational complexity typically associated with Transformer architectures, Ensure that the attention calculation of the model remains within the linear complexity range; Experiments were conducted on the Scene Flow and KITTI-2015 datasets, and the indicators were significantly improved; The effectiveness and correctness of the model were verified through comparative experiments.