In order to improve the robustness and correct recognition rate of hash sequences against geometric attacks, a robust hash algorithm based on multi-directional mutual cross pattern operator and polar coordinate transformation is designed in this paper. By introducing interpolation operation and gaussian filter, the size normalization and denoising of the image are completed so that it can output a fixed-length hash sequence for any suspicious target. And the filtered standard image is processed based on polar coordinate transformation to output the secondary image against rotation attack. Subsequently, the secondary image is transformed into two coding maps from eight directions by using the multi-directional mutual cross pattern operator. Two coding maps are segmented into non-overlapping sub-blocks, and the histograms of these sub-blocks are extracted to treat as texture features as the first hash sequence. The SURF method is improved by replacing the intensity gradient with the intensity probability density gradient to fully extract the stable corners in the image for forming the corner image. The corner image is divided into a series of non-overlapping sub-blocks, and the sub-blocks with the richest structural information are marked by calculating the number of corners contained in each sub-block to output their corresponding position information in the image. And the low frequency coefficients corresponding to each sub-block are obtained by decomposing these marker sub-blocks with the help of discrete wavelet transform. The structural features are formed by combining location information with low frequency coefficients, which as the second hash sequence. The encryption mechanism is designed to diffuse the two hash sequences for forming the final hash sequence. By calculating the l2 norm distance between the source image and the suspicious image, the authenticity of the target is judged based on comparing it with the user recognition threshold. The experimental data show that the proposed method has higher robustness and recognition accuracy for various geometric attacks than the existing hash generation mechanism.