Stereo matching is a key step in binocular stereo vision to perceive the depth information of the scene, and in view of the difficulty of the traditional binocular stereo matching algorithm to effectively solve the problem of matching ambiguity in weak texture areas and complex lighting scenes, a cross-scale stereo matching algorithm combining the texture characteristics of the scene was proposed. Firstly, the left and right images are downsampled by Gaussian to obtain image pairs of multiple scales as the input images of the algorithm, and then the cost calculation of image pairs of different scales is carried out to obtain the initial cost body. Based on the texture characteristics, the input image is divided into texture-rich region and weak-texture region, and the initial cost body is difed at each scale according to the texture region, and the matching cost of the texture-rich region is diffused to the weak-texture region. The optimization guidance filtering algorithm is used to aggregate the cost of the parallax map of each scale. Considering the multi-scale interaction between the cost bodies, the cost fusion is carried out to obtain the final cost body. Subsequently, the final disparity map is obtained by parallax calculation and parallax post-processing. The test results of the dataset of Middlebury website show that after the introduction of the cross-scale stereo matching algorithm combined with the characteristics of texture regions, the mismatching rate of all regions is reduced by 2.35% on average compared with the guided filtering algorithm. Compared with the CSCA algorithm, it is reduced by 0.77% on average. Compared with the guided filtering algorithm, the mismatching rate of the unoccluded region is reduced by 2.29% on average. Compared with the CSCA algorithm, it is reduced by 0.65% on average. It shows that the proposed algorithm can effectively solve the problem of mismatching in weak texture regions, and meet the requirements of high efficiency and high precision in the process of stereo matching.