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To enhance the performance of image classification and improve the accuracy and efficiency of copy detection, this paper proposed an image hash algorithm based on QBFM moments and three-dimensional structure. Firstly, it used normalization to process the color image, and obtained the Gaussian fusion image and Laplace fusion image through multi-scale fusion, then extracted the QBFM features of two fusion images. At the same time, it directly extracted gradient information of the Gaus-sian fusion image in the RGB color space and constructed a three-dimensional model, used the concave convex point information of peak and valley curve of gradient from different perspectives to obtain the three-dimensional local structure features. Then, it disposed the three-dimensional model of gradient image by equidistant segmentation, Counted the number of pixels and variance of each section as the three-dimensional global structure features. Finally, it combined the QBFM features and three-dimensional features of the image and scrambled to form the final hash sequence. Experimental results show that the algorithm has a better balance between robustness and discrimination. Compared with the existing hash algorithms, it has good image classification performance. In the copy detection experiment, the algorithm has the best recall and precision. [ABSTRACT FROM AUTHOR]

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