It is difficult in the image recognition technology with different rotation angles and the change of image matching, and SURF algorithm in multi-angle feature matching process has more noise, easy to mismatching, and matching efficiency is low. Combination with cluster and Mahalanobis distance, this paper proposes an improved multi-angle SURF image matching algorithm. First, it uses clustering algorithm to eliminate the noise, to the feature point SURF algorithm extracted, uses clustering algorithm to classify and remove noise to get the new feature point data set. Then, it uses Mahalanobis distance’s characteristics that it considers the overall correlation, and has the characteristics of affine invariance, replacing Euclidean distance with Mahalanobis distance. When the experiment is applied to multi-angle image matching, compared with the original SURF, the improved algorithm has obviously improved on the matching efficiency.