The development of artificial intelligence brings more and more challenges to data hiding technology, and it is urgent to improve the security of existing steganography methods. In this study, a generative multiple adversarial steganography algorithm based on U-Net network structure is proposed to improve the image data hiding ability. A generative multiple adversarial steganography network (GMASN), including the generative adversarial network, the steganalyzer optimization network and the steganalysis network, is firstly constructed, and the anti steganalysis ability of the steganography image is improved through the competition of the networks in the GMASN. At the same time, aiming at the problem that the existing generative adversarial network can only generate low-quality images randomly, a generative network based on U-Net structure is designed to transfer the details of the reference image to the generated carrier image, by which the image can be generated objectively with high visual quality. Moreover, the image discrimination loss, mean square error (MSE) loss, and steganalysis loss are dynamically combined in the proposed scheme to enable the GMASN to converge rapidly and stably. Experimental results show that the PSNR of the generated carrier image can reach 48.60 dB, and the discrimination rate between the generated carrier image and the steganographic image is 50.02%. The proposed algorithm can generate high-quality carrier images suitable for data hiding, enable the steganographic network to converge rapidly and stably, and improve the security of image steganography effectively.