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Aiming al the retrieval semantic information in Mongolian, this paper proposed a new method combined topic model latent dirichlet allocation (LDA) and language model. This method modeled Mongolian documents with LDA and language model, estimated parameters with Gibbs sampling and represented probability of word, it could mine the hidden relationship between the different topics and the words from documents, got the topic distribution and computed the similarity of keywords topics. Finally, it returned to the most relevant documents with topics. Experimental results show that the method has a higher performance in topic semantic compared with one sole model. [ABSTRACT FROM AUTHOR]