In order to solve the problems of SIFT algorithm in stereo vision, such as long time consuming, high degree of mismatch and feature points clustering, a fast feature matching algorithm based on quadtree fusion of sift and k-d tree is proposed. This method uses a fast feature point extraction algorithm combined with adaptive threshold to extract the key points. Because the extracted key points have the phenomenon of clustering, a quadtree structure is proposed and applied to image matching. The improved k-d tree and random consistency algorithm are used to rough match and purify the key points. Experimental results show that the average matching rate of the improved algorithm is 3.35 times higher than that of SIFT algorithm, and the matching accuracy is improved from 86.18% to 97.53%. At the same time, the improved algorithm has more advantages than SIFT algorithm in view angle, blur, illumination and scale change, so the algorithm can meet the requirements of high matching degree, good real-time performance and uniform feature points.