This study proposes a new cross-media retrieval model of deep hash to solve the unreasonable distribution of the hash codes of semantically similar media objects in Hamming space in the existing retrieval methods. In this model, the cross-media association loss of deep hash is improved by the Cauchy distribution in Hamming space, making the hash codes of semantically similar media objects in a short distance and those of semantically dissimilar ones far apart. Thus, the retrieval effect of the model is improved. Furthermore, an efficient model-solving method is presented in this study, and the approximate optimal solution of the model is obtained by alternating iteration. The experimental results on Flickr-25k, IAPR TC-12, and MS COCO datasets show that this method can effectively improve the performance of cross-media retrieval.