Entity resolution （ER） is a task to identify whether several records correspond to the same entity in the real world， which is a key problem in data cleaning and data integration. Recently， deep learning-based entity resolution is popular， which requires a large number of labeled data to achieve better results. However， a large number of high-quality labeled data are not always easily available in the real scenario. This paper proposes a deep transfer learning-based entity resolution model. The common features of the source domain and the target domain are extracted through a domain separation network. ER results are obtained by utilizing these common features. Therefore， the common features are transferred from the source domain to the target domain. The experimental results show that， on several datasets， the proposed method has a maximum improvement of about 40% in the F1 metric compared with the previous best method. Experiments show that the proposed method has superior performance and shorter training time.