Electronic　medical　records　(EMRs)　have　high　value　for　research,　as　they　contain　the　patient＇s　personal　information,　medical　history,　clinical　examination,　treatment　process,　and　other　information.　Analysis　based　on　EMRs　can　effectively　assist　doctors　in　clinical　decision-making,　provide　data　support　for　clinical　research　as　well　as　personalized　healthcare　service　for　patients.　We　introduce　a　novel　approach　for　EMR　similarity　computation　by　re-structuring　and　filtering　some　parts　of　physical　examination　result.　Our　approach　is　motivated　by　observations　that　it　is　easier　to　distinguish　disease　bias　special　part　than　bias　the　whole　EMR　which　maybe　contain　some　ineffectiveq　information.　Assuming　the　check　parts　are　independent,　we　split　them　and　select　effective　parts.　Then,　we　apply　Deep　NLP,　converting　the　word　to　vectors　which　can　be　used　to　measure　syntactic　and　semantic　word　similarities　better.　In　addition,　We　replace　traditional　Euclidean　distance　with　Word　Mover＇s　Distance(WMD),　a　novel　distance　function　between　text　documents.　Finally,　KNN　cluster　is　been　used　to　evaluate　the　similarity　between　EMRs.　Compared　with　traditional　method　such　as　LDA　and　LSI,　our　proposed　method　achieved　higher　recall　value　of　disease　classification　problem.