On the premise of ensuring the properties of nodes in the network， the purpose of attribute network representation learning is to learn the low-dimensional dense vector representation of nodes by combining structure and attribute information. In the existing attribute network representation learning methods， the learning of attribute information in the network is ignored， and the interaction of attribute information with the network topology is insufficient， so that the network structure and attribute information cannot be fused efficiently. In response to the above problems， a Dual auto-Encoder Network Representation Learning （DENRL） algorithm was proposed. Firstly， the high-order neighborhood information of nodes was captured through a multi-hop attention mechanism. Secondly， a low-pass Laplacian filter was designed to remove the high-frequency signals and iteratively obtain the attribute information of important neighbor nodes. Finally， an adaptive fusion module was constructed to increase the acquisition of important information through the consistency and difference constraints of the two kinds of information， and the encoder was trained by supervising the joint reconstruction loss function of the two auto-encoders. Experimental results on Cora， Citeseer， Pubmed and Wiki datasets show that DENRL algorithm has the highest clustering accuracy and the lowest algorithm running time on three citation network datasets compared with DeepWalk， ANRL （Attributed Network Representation Learning） and other algorithms， achieves these two indicators of 0.775 and 0.460 2 s respectively on Cora datasets， and has the highest link prediction precision on Cora and Citeseer datasets， reaching 0.961 and 0.970 respectively. It can be seen that the fusion and interactive learning of attribute and structure information can obtain stronger node representation capability.