Abstract: Abstract: Node label is a widely existed supervision information in complex networks, and it plays an important role in network representation learning. To solve this problem, a Semi-supervised Representation Learning method combining Graph Auto-Encoder and Clustering (GAECSRL) was proposed. First, the graph convolutional network and the inner product function were used as the encoder and the decoder respectively, and the graph auto-encoder was constructed to form an information dissemination framework; then, the k-means clustering module was superimposed on the low-dimensional representation generated by the encoder, so that the training process of the graph auto-encoder and the classification of the node's category distribution formed a self-supervised mechanism; finally, the classification information of the low-dimensional representation of the network was guided by using the discriminant information of the node labels. The network representation generation, category division, and the training of the graph auto-encoder were built into a unified optimization model, and finally an effective network representation result that integrates node label information could be obtained. In the simulation experiment, the GAECSRL method was used for node classification and link prediction tasks. The experimental results show that, compared with DeepWalk, node2vec, GraRep(Learning Graph Representations with Global Structural Information), SDNE (Structural Deep Network Embedding) and Planetoid (Predicting Labels and Neighbors with Embeddings Transductively Or Inductively from Data), the Micro-F1 index of GAECSRL in the node classification task increased by 0.9~24.46 percentage points, and the Macro-F1 index increased by 0.76~24.20 percentage points; in the link prediction task , the AUC index increased by 0.33~9.06 percentage points, indicating that the network representation results obtained by GAECSRL can effectively improve the performance of node classification and link prediction tasks.