The work aims to solve the problems of many missing matching points and wrong matching points of the algorithm because the current image matching algorithm mainly achieves the feature matching by the distance between the feature points, thus ignoring the structural features of the feature point. An image matching algorithm based on invariant moment feature model and coupling similarity measurement rules was proposed. The detection rules were formulated by the classification of neighborhood circle points constructed by the pixel points to be detected, and the FAST descriptor was improved. The improved FAST descriptor was used to detect the feature points of the image quickly and precisely. Subsequently, the invariant moment feature model was constructed to replace the method for obtaining the feature vectors from SIFT algorithm to generate feature descriptors of lower dimension. Euclidean model and SSIM model were used to establish similarity measurement rules for the feature matching of images. Finally, the Random Sample Consensus (RANSAC) algorithm was introduced to eliminate the wrong matching points and complete the image matching. The simulation results showed that, compared with the current image matching algorithm, the proposed algorithm had higher matching accuracy and stronger robustness. Its maximum recall ratio could be around 95%, and it had faster matching efficiency, approximately 3.75 s. With good matching accuracy, the proposed matching method has certain reference value in the fields of image information security, packaging barcode recognition and splicing, etc.