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.