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The widespread use of social media makes short texts clustering an important research topic. In order to solve the problem that the traditional text clustering method do not well in short text caused by the high dimension and sparseness of the short text word vector. It find that due to the sparseness of the short text, the word discriminative ability is particularly important for the study of short text class structures. In the classical text clustering model LDA( latent Dirichlet allocation), BTM( biterm topic model) and GSDMM( Gibbs sampling dirichlet multinomial mixture model), it added the binomial distribution to measure the discriminant ability in the class structure learning, and solved the parameters in the model by Gibbs sampling algorithm. Finally, the experimental results on the real data set show that the learning of the discriminant ability can improve the accuracy, mutual information and F value of the existing probability model clustering method. And it studies the validity of the study of word discrimination. [ABSTRACT FROM AUTHOR]