Stereo matching algorithms used currently are ineffective at matching weak textures, shadows, and other pathological regions. To make full use of scene context information to improve disparity matching accuracy, this study proposes an effective multiple attention stereo matching algorithm (MAnet). At the feature extraction stage, according to multiple attention mechanisms, such as location channel attention and multiheads crisscross attention (MCA), we adjust the feature channels and selectively aggregate contextual information in any range to provide more discriminative features for matching cost calculation. Extending MCA to 3D convolution expands the network perceptual region to accumulate more precise matching cost. The learning ability of challenging regions is enhanced for the networks’ loss function by weighting the loss outside the error threshold. The algorithms are experimentally validated on the KITTI dataset, and the error of MAnet for the KITTI2015 test set is 2.06%. The experimental findings demonstrate that compared to the benchmark algorithm, the MAnet enhances disparity accuracy and improves the matching performance in the pathological region.