Sentiment analysis aims to extract users' sentimentsand opinions about or their attitude towarda specific product,service,or event.The lack of labeled data is a significant challenge in sentiment analysisand will deteriorate the performance of the classifierin a supervised sentiment analysis task.The cross-domain approach has been shown to be effective in addressing this problem.However,the inherent difference between the source and target domains will make it difficult for the classifier to be adaptive to the target domain.In this paper,we propose a novel method to use the available labeled data,however few they may be,in the target domain to enhance the domain adaption.Specifically,we present a cross-domain sentiment classification model using the capsule network.Based on this architecture,we design extra capsule layers for domain adaption.Extensive experiments with real-world datasets prove that our proposed model outperforms baselines by a large margin.