Abstract: Recently, studies on domain adaptation have shown the effectiveness of adversarial learning in filling the differences between two domains, but there are still some limitations that samples taken from two domains are not enough to keep the domain invariance in potential spaces. Inspired by the fact that CapsNet(capsule network) has a strong ability to extract the invariance of features from samples, we introduce it into the domain adaptation problem. Firstly, a new convolution algorithm is devised over capsule-layer, combined with residual block, which makes it possible to train a deeper CapsNet. Results demonstrate that this new structure of CapsNet has a stronger ability to extract features. Secondly, the traditional adversarial discriminative adaptation methods have the defect that prones to blur the boundary between different domains, which in turn leads to a decline in the discriminative performance. Inspired by VAE-GAN(variational auto-encoder, generative adversarial networks), we use a reconstruction network as a strong constraint, so that the adversarial discriminative network avoids the inherent defect of mode collapse when the convolution base is transferring, and ensures that the discriminator is sensitive to the invariance of representation in different domains. Experiments on standard datasets show that our model achieves better performance in domain adaptation tasks of varying complexity.