Aiming at the problem that the existing stereo matching algorithm has high mismatch rate in ill-posed regions such as weak texture, repeated texture and reflective surface, a new pixel attention siamese neural network is proposed. Our method consists of siamese attention hourglass subnetwork and attention U-shaped subnetwork . Firstly, the feature map of the input image is extracted by the siamese attention hourglass subnetwork. Secondly, the cost matrix of the feature graph is obtained through the correlation layer. Finally, the cost matrix is aggregated by the attention U-shaped subnetwork, and the disparity map is output. Experiments on the KITTI dataset demonstrate that the proposed algorithm can effectively solve the ill-posed problem and improve the stereo matching accuracy.