Unsupervised stereo matching algorithms have important applications in areas such as autonomous driving, however, unsupervised stereo matching algorithms have low disparity accuracy in the region of object continuity, edges and other detail information, this paper proposes a new unsupervised stereo matching algorithm to improve the accuracy of detail information region by combining spatial attention mechanism and parallax fusion. Specifically, the spatial feature pyramid network is designed by introducing a spatial attention mechanism and residual structure into the feature pyramid network, to suppress the loss of edge and small target details in the process of feature extraction. Further, a disparity fusion module is constructed to improve the accuracy of the continuous detail information region, where the original disparity generated by the semi-global block matching algorithm and the initial disparity generated by disparity regression are fused with confidence disparity. Moreover, For the network loss function, the original disparity supervised loss and confidence masking loss are integrated to retain more detailed information at image edges and continuous regions. Finally, the experimental results show that the mis-matching rate of the proposed algorithm in the non-occluded region and all regions in the KITTI 2015 test set are 6.24% and 5.89%, respectively, which greatly improves the effect and accuracy of the detailed information region compared with other classical algorithms.