Aiming at the problems that the high-capacity steganography model based on encoding-decoding network has weak robustness and can not resist noise attack and channel compression， a high-capacity robust image steganography scheme based on encoding-decoding network was proposed. In the proposed scheme， encoder， decoder and discriminator based on Densely connected convolutional Network （DenseNet） were designed. The secret information and the carrier image were jointly encoded into a steganographic image by the encoder， the secret information was extracted by the decoder， and the discriminator was used to distinguish between carrier images and steganographic images. A noise layer was added between the encoder and the decoder； Dropout， JPEG compression， Gaussian blur， Gaussian noise and salt and pepper noise were used to simulate a real environment with various kinds of noise attacks. The steganographic image output by the encoder was processed by different kinds of noise and decoded by the decoder. Through training the model， the secret information could be extracted from the noise-processed steganographic image by the decoder， so that the noise attacks could be resisted. Experiment results show that the steganographic capacity of the proposed scheme reaches 0.45 - 0.95 bpp on 360×360 pixel images， and the relative embedding capacity is improved by 2.04 times compared to the suboptimal robust steganographic scheme； the decoding accuracy reaches 0.72 - 0.97， and compared with the steganography without noise layer， the average decoding accuracy is improved by 44 percentage points. The proposed scheme not only guarantees high embedding quantity and high coding image quality， but also has stronger anti-noise capability.