Stereo matching is an important research direction in the field of binocular vision. In order to ensure the matching accuracy of image texture regions and reduce the mismatch rate of weak texture regions, this paper proposes a stereo matching method based on guided filtering and disparity map fusion based on traditional guided filtering. First, the image is divided into areas with rich texture and areas with weak texture according to the similarity of image colors. Then, two disparity maps are obtained by cost aggregation and disparity calculation using guided filtering with different parameters. Then, the two disparity maps obtained are merged according to the result of texture region division. Finally, the final disparity map is obtained through disparity optimization steps such as left and right consistency detection and weighted median filtering. Experiments on standard image pairs on the Middlebury test platform show that the average mismatch rate of this method on 6 sets of weak texture images is 9.67%, which has a higher matching accuracy than the traditional guided filter stereo matching algorithm.