The work aims to solve the problem that the measurement is currently done mainly by depending on the extraction of feature vector, thus causing many mismatching points in the matching results and the increasingly bigger matching errors during the matching by means of its maximum point of the corresponding correlation coefficient. An image matching algorithm based on the regional gray level distribution coupling similarity determination strategy was proposed. First of all, the feature points of the image were extracted with the Forstner operator. With the feature points as the center, the main direction of the feature points was determined by establishing the polar coordinate system. The feature descriptor was generated through the neighborhood gray feature of the feature points. Then, the similarity between the feature points was evaluated by introducing the normalized cross correlation (NCC) function. The bidirectional matching rule of a rectangular window feature point was established to complete the feature point matching, in order to improve the matching accuracy and robustness of the algorithm between the feature points. Finally, a similarity determination strategy was designed based on the similarity of triangles formed by accurately matched feature points, which was used to eliminate the mismatching points, so as to improve the matching accuracy. The experimental results showed that, compared to the current image matching technology, the matching algorithm herein had higher matching accuracy and efficiency, which effectively reduced the mismatching rate of the feature points. The proposed image matching technology has higher registration accuracy and provides certain application value in such fields as image forgery and packaging bar code recog-nition.