In relation extraction,directly adopting a model trained in the source domain to the target domain will suffer greatly performance decrease.Existing studies extract the shared features between domains in a coarse-grained way,which inevitably introduce some domain-specific features or suffer from information loss.Inspired by human beings often using different views to find connection between domains,we argue that,there exist some fine-grained features which can be shared across different views of origin data.In this paper,we proposed a cross-view adaptation network,which use adversarial method to extract shared features and introduce cross-view training to fine-turn it.Besides,we construct some novel views of input data for cross-domain relation extraction.Through experiments we demonstrated that the different views of data we con-struct can effectively avoid introducing some domain-specific features into unified feature space and help the model learn a fine-grained shared features of different domain.On the three different domains of ACE 2005 dataset,Our method achieved the state-of-the-art results in F1-score.