In order to solve the problems of poor robustness and high mismatch rate caused by the noise, illumination and scale in the image feature matching process, a feature matching method based on Weighted Similarity Measurement (WSM) was proposed. At first, FM_GMC (Feature Matching based on Grid and Multi-Density Clustering) algorithm was adopted to divide image into several feature clustering blocks. Secondly, in each feature clustering block, the edge feature points were attracted by Canny and descripted by Scale-Invariant Feature Transform (SIFT). Thirdly, the similarity measurements were performed on the Hausdorff distance of spatial context information between feature clustering blocks, the Euclidean distance between appearance descriptors of image feature points and Normalized Cross Correlation (NCC) by using weighting methods. Finally, the similarity measurement results were further optimized according to Nearest Neighbor Distance Ratio (NNDR), so as to determine the feature matching result. With the ancient architecture images as the dataset, the experimental results show that the WSM method has an average matching precision of 92%, and is superior to commonly matching algorithms on matching number and matching precision. Therefore, the effectiveness and robustness of WSM method are verified.