It is an important task to carry out reasoning and calculation on graph structure data. The main challenge of this task is how to represent graph-structured knowledge so that machines can easily understand and use graph structure data. After comparing the existing representation learning models， it is found that the models based on random walk methods are likely to ignore the special effect of attributes on the association between nodes. Therefore， a hybrid random walk method based on node adjacency and attribute association was proposed. Firstly the attribute weights were calculated through the common attribute distribution among adjacent nodes， and the sampling probability from the node to each attribute was obtained. Then， the network information was extracted from adjacent nodes and non-adjacent nodes with common attributes respectively. Finally， the network representation learning model based on node attribute bipartite graph was constructed， and the node vector representations were obtained through the above sampling sequence learning. Experimental results on Flickr， BlogCatalog and Cora public datasets show that the Micro-F1 average accuracy of node classification by the node vector representations obtained by the proposed model is 89.38%， which is 2.02 percentage points higher than that of GraphRNA （Graph Recurrent Networks with Attributed random walk） and 21.12 percentage points higher than that of classical work DeepWalk. At the same time， by comparing different random walk methods， it is found that increasing the sampling probabilities of attributes that promote node association can improve the information contained in the sampling sequence.