In order to improve the accuracy of homography estimation and solve the problem that it is difficult to obtain real labels, an unsupervised homography estimation algorithm with correction function is proposed. The algorithm adopts a cascade structure, and its idea is similar to iteration, in which each level network maintains the same number of layers and parameters, and the homography of the output of the next level network is estimated as the residual of the sum of the real matrix and the previous output homography matrix. Considering the requirements of model complexity and real-time, a two-level network cascade is adopted in this paper. Through the verification on 5000 images in the COCO dataset, it shows that the cascade unsupervised algorithm designed in this paper has more accurate estimation ability than traditional methods and other methods based on deep learning. Its average pixel error in the test set is 0.54, which is 95.38% lower than that of traditional methods, and the running speed reaches 95f/s.