Network representation learning is regarded as a key technology for improving the efficiency of information network analysis. It maps network nodes to low-dimensional vectors in a latent space and maintains the structure and characteristics of the original network in these vectors efficiently. In recent years, many studies focus on exploring network topology and node features intensively, and the application bears fruit in many network analysis tasks. In fact, besides these two kinds of key information, the accompanying information widely existing in the network reflects various complex relationships and plays an important role in the network’s construction and evolution. In order to improve the efficiency of network representation learning, a novel model integrating the accompanying information is proposed with the name NRLIAI. The model employs the variational auto-encoders (VAE) to propagate and process information. In addition, it aggregates and maps network topology and node features by graph convolutional operators in the encoder, reconstructs the network in the decoder, and integrates the accompanying information to guide the network representation learning. Furthermore, the proposed model solves the problem that the existing methods fail to utilize the accompanying information effectively. At the same time, the model possesses a generative ability, which enables it to reduce the overfitting problem in the learning process. With several real-world network datasets, this study conducts extensive comparative experiments on the existing methods of NRLIAL through node classification and link prediction tasks, and the experimental results have proved the feasibility of the proposed model.