The invention discloses a cross-modal retrieval method based on a cyclic generation antagonistic network. The method designs a novel dual-channel cyclic generation antagonistic neural network and establishes semantic correlation of cross-modal data by training the neural network. Given different modal data can flow bi-directionally in the network, each modal data can generate another modal data through a group of generative antagonistic networks, and the generated data can be used as the input of the next group of generative antagonistic networks, so that the bi-directional loop generation ofdata can be realized, and the network continuously learns the semantic relationship among the cross-modal data. In order to improve the efficiency of retrieval, The method also uses threshold functionand approximation function to approximate the result of the middle layer of the generator to the corresponding binary hash code, and designs a variety of constraints to ensure the same mode, the similarity and cross-mode of the same kind of data, and the difference of data between classes, thus further improving the accuracy and stability of retrieval.