Abstract: In order to improve the performance of semi-supervised classifier, a kind of semi-supervised classification algorithm SSC-SOM is proposed. Based on the clustering characteristics of SOM and the Cluster-then-Label idea, labeled data and unlabeled data are all used to train SOM. The labeled samples are assigned to each cluster and the clusters form simultaneously. The clustering centers are work out. K-NN algorithm is adopted to label the unlabeled samples according to the clustering centers and the information from the unlabeled samples is mined. With UCI dataset, experiments were carried out and the results show that the classification rate of SSC-SOM increases by 2.22% than SSOM and the SSC-SOM method had good convergence.