Through analyzing the characteristics of the text, a novel text clustering approach based on Non-negative Matrix Factorization with sparseness constraint (NMF_SC) is presented. The method uses NMF_SC decomposing word-text matrix to reduce the dimension of the feature space, and better controls sparsity with sparseness constraint, and then further refines clusters by using the similarity of documents in clusters. Compared with text clustering method based on k-means and text clustering method based on NMF, the results of experiment show that the method has high value of the normalized mutual information, thus it has good clustering performance.