Existing methods for named entity recognition (NER) are critically relied on the amount of labeled data. However, these methods suffer from performance decline in a new domain which is fully-unlabeled. To handle the situation, we propose an entity-aware adversarial domain adaptation network, which utilizes the labeled data from source domain and then adapts to unlabeled target domain. We first apply adversarial training to reduce the distribution gap between different domains. Furthermore, we introduce an entity-aware attention to guide adversarial to achieve the alignment of entity features. The experimental results show that our model outperforms the state-of-the-art approaches.