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 ratio of weak texture regions. A stereo matching method is proposed based on guided filtering and disparity map fusion. First, the image is divided into areas with rich texture and areas with weak texture according to the similarity of image colors. Secondly, two disparity maps are obtained by cost aggregation and disparity calculation by using guided filtering with different parameters. Then, the two obtained disparity maps 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 performed 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%, the matching accuracy is much higher than that of the traditional guided filter stereo matching algorithm.