Document　similarity　computation　is　an　exciting　research　topic　in　information　retrieval　(IR)　and　it　is　a　key　issue　for　automatic　document　categorization,　clustering　analysis,　fuzzy　query　and　question　answering.　Topic　model　is　an　emerging　field　in　natural　language　processing　(　NLP),　IR　and　machine　learning　(ML).　In　this　paper,　we　apply　a　latent　Dirichlet　allocation　(LDA)　topic　modelbased　method　to　compute　similarity　between　documents.　By　mapping　a　document　with　term　space　representation　into　a　topic　space,　a　distribution　over　topics　derived　for　computing　document　similarity.　An　empirical　study　using　real　data　set　demonstrates　the　efficiency　of　our　method.