Learning the latent vector representations of nodes in the graph is an important and ubiquitous task， which aims to capture various attributes of the nodes in the graph. A lot of work demonstrates that static graph representation learning can learn part of the node information； however， real-world graphs evolve over time. In order to solve the problem that most dynamic network algorithms cannot effectively retain node neighborhood structure and temporal information， a dynamic network representation learning method based on Deep Neural Network （DNN） and Gated Recurrent Unit （GRU）， namely DynAEGRU， was proposed. With Auto-Encoder （AE） as the framework of the DynAEGRU， the neighborhood information was aggregated by encoder with a DNN to obtain low-dimensional feature vectors， then the node temporal information was extracted by a GRU network，finally， the adjacency matrix was reconstructed by the decoder and compared with the real graph to construct the loss. Experimental results on three real-word datasets show that DynAEGRU method has better performance gain compared to several static and dynamic graph representation learning algorithms.