To solve the problem of Insufficient matching accuracy of the weak texture and disparity discontinuity regions in the image, A stereo matching algorithm combining superpixel segmentation and cross-scale PatchMatch is proposed. Firstly, multi-scale images are obtained by the gaussian under-sampling, and superpixel segmentation of each scale image. Then, based on the four-color theorem, corroding superpixel boundaries makes the iterative propagation of 3D labels on superpixels sub-modular and independent, and the generated sub-modular energy is optimized by the Graph Cut (GC) algorithm. Finally, aim to make 3D label iterative propagation can be cross-scale GC optimization to obtain the optimal disparity map, a cross-scale energy function model is proposed to constrain the consistent energy of 3D labels of the same pixel at different scales. Experimental results on Middlebury data set show that the average mismatch rate of the proposed algorithm for 21 groups of weak texture and complex texture images is 2.20%. Compared with other improved PatchMatch stereo matching algorithm, the false matching rate is reduced by 10.1%. Visualization of disparity map mismatched regions shows the proposed algorithm is better than other improved PatchMatch stereo matching for weak texture and disparity discontinuity regions algorithm.