Network representation learning called NRL for short aims at embedding various networks into low-dimensional continuous distributed vector spaces.Most ex-isting representation learning methods focus on learning rep-resentations purely based on the network topology,i.e.,the linkage relationships between network nodes,but the nodes in lots of networks may contain rich text features,which are beneficial to network analysis tasks,such as node classifi-cation,link prediction and so on.In this paper,we propose a novel network representation learning model,which is named as Text-Enhanced Network Representation Learning called TENR for short,by introducing text features of the nodes to learn more discriminative network representations,which come from joint learning of both the network topology and text features,and include common influencing factors of both parties.In the experiments,we evaluate our proposed method and other baseline methods on the task of node classification.The experimental results demonstrate that our method outper-forms other baseline methods on three real-world datasets.