The aim of Network Representation Learning （NRL） is to learn the potential and low-dimensional representation of network vertices， and the obtained representation is applied for downstream network analysis tasks. The existing NRL algorithms using autoencoder extract information about node attributes insufficiently and are easy to generate information bias， which affects the learning effect. Aiming at these problems， a Network Representation learning model based on Autoencoder with optimized Graph Structure （NR-AGS） was proposed to improve the accuracy by optimizing the graph structure. Firstly， the structure and attribute information were fused to generate the joint transition matrix， thereby forming the high-dimensional representation. Secondly， the low-dimensional embedded representation was learnt by an autoencoder. Finally， the deep embedded clustering algorithm was introduced during learning to form a self-supervision mechanism in the processes of autoencoder training and the category distribution division of nodes. At the same time， the improved Maximum Mean Discrepancy （MMD） algorithm was used to reduce the gap between distribution of the learnt low-dimensional embedded representation and distribution of the original data. Besides， in the proposed model， the reconstruction loss of the autoencoder， the deep embedded clustering loss and the improved MMD loss were used to optimize the network jointly. NR-AGS was applied to the learning of three real datasets， and the obtained low-dimensional representation was used for downstream tasks such as node classification and node clustering. Experimental results show that compared with the deep graph representation model DNGR （Deep Neural networks for Graph Representations）， NR-AGS improves the Micro-F1 score by 7.2， 13.5 and 8.2 percentage points at least and respectively on Cora， Citeseer and Wiki datasets. It can be seen that NR-AGS can improve the learning effect of NRL effectively.