Abstract: An information transfer classification algorithm based on semi-supervised learning is proposed in this paper to partition the network with labeled nodes and unlabeled nodes into different clusters.The classification parameters of all unlabeled nodes in network are firstly determined in terms of the suggested approach，and the clustering results can be obtained by iteratively computing these parameters.The analysis of experimental data has proven that this algorithm has better effect on semi-supervised classification.