The work aims to propose an image matching algorithm based on information characteristics and coupling angle consistency rule regarding the problems of many mismatches and missing matches caused by current image matching algorithm based on grayscale feature when there is a large illumination transformation in matching images. First of all, the Forstner operator was used to detect the feature points of the image, and then the ratio of the maximum eigenvalue to the minimum eigenvalue of the Hessian matrix was calculated to optimize the feature points detected by the Forstner descriptor. Then, the polar coordinate system was constructed with the feature points as the origin, and the neighborhood of the feature points was segmented. The information feature of each segmentation block was obtained with the information entropy model to generate the feature descriptor. Finally, the distance model was constructed by feature descriptor, and the nearest neighbor feature points and the next nearest neighbor feature points of the specified feature points were searched, and the feature points were matched by distance ratio method. By matching the angle formed between the feature points, an angle consistency rule was established to measure the reliability of the matched feature points, and the wrongly matched feature points were eliminated to complete the image matching. The experimental results showed that, compared with the current image matching algorithm, when the rotation angle was 10~100, the recognition rate was 94.6%~88% and the average recognition time was 5.48 s, the proposed image matching algorithm had higher matching accuracy and stronger robustness. The proposed algorithm has high detection accuracy and good application value in the field of printing anti-counterfeiting and information security.