Different clustering algorithms are used to design their own strategies. However,each technology has certain limitations when it executes a particular dataset. An adequate choice of Discrimination Information Method(DIM)can ensure the document clustering. To solve these problems,a DIM of Document Clustering based on Consensus and Classification (DCCC) was proposed. Firstly,Clustering by DIM (CDIM) was used to solve the generation of initial clustering for dataset,and two initial cluster sets were generated by two different CDIMs. Then,two initial cluster sets were initialized again by different parameter methods,and a consensus was established by using the relationship between the cluster label information,so as to maximize the sum of documents' discrimination number. Finally,Discrimination Text Weight Classification(DTWC)was chosen as text classifier to assign new cluster label to the consensus,the base partitions were altered by training the text classifier,and the final partition was obtained based on the predicted label information. Experiments on 8 network datasets for clustering verification by BCubed's precision and recall index were carried out. Experimental results show that the clustering results of the proposed consensus and classification method are superior to those of comparison methods.