An improved SURF-based image matching algorithm is proposed for the traditional SURF image matching algorithm which has the problems of complex computing data, time-consuming, and poor matching accuracy. Firstly, the traditional SURF algorithm is employed to extract the feature points of the image to be matched, and then the 64-dimensional descriptor of SURF is reduced to 20 dimensions by replacing the rectangular area with a circular area. Secondly, the KNN algorithm is utilized to bidirectionally match the feature points of the image to be matched, and the matching pair set of bidirectional initial feature points is obtained. Finally, the mismatching pairs of initial matching points are eliminated bidirectionally by the RANSAC algorithm. The experimental results show that the proposed algorithm reduces the detection time, improves the matching accuracy, and has strong robustness.