The work aims to design an image matching algorithm based on Gauss curvature model and correlation constraint rule with respect to such deficiencies as low matching accuracy and robustness induced by ignoring the correlation between feature points in the matching process of many current image matching algorithms. First, the first-order matrix of the image after the Gauss filter and the Hessian matrix were used to construct the Gauss curvature model, and the Hessian operator was improved to fully detect the feature points of the image. Then, the main direction of the feature points was obtained by obtaining the Haar wavelet response in the sector area, and the eigenvectors were calculated according to the average gray-value of the pixel points in the neighborhood of the feature points, and thus the feature descriptors were formed to complete the description of feature points. The correlation model was constructed based on the mean and covariance matrix of the feature point set and the relevancy of feature points was measured. The similarity of the feature points was judged, and the matching of the feature points was completed. RANSAC algorithm was used to purify the matching feature points and complete the matching of images. The simulation results showed that, compared with the current image matching algorithm, the proposed algorithm not only had higher matching accuracy, but also had stronger robustness, whose correct matching accuracy could still reach over 87% when the rotation angle was 50°. The proposed algorithm still has higher matching accuracy under various geometric attacks, which has good reference value in image processing, information security and other fields.