The　physique　of　traditional　Chinese　medicine　(TCM)　is　the　quality　of　our　body　and　the　tongue　image　is　a　manifestation　based　on　the　metabolism　of　the　body.　The　constitution　can　be　easily　and　objectively　identified　by　the　image　of　the　tongue.　In　this　paper,　the　classical　convolution　neural　network　(CNN)　and　gray　level　co-occurrence　matrix,　minimum　enclosing　rectangle　and　edge　curve　are　used　to　extract　the　features　of　human　tongue.　Then　different　classifier　are　used　to　classify　different　constitution,　and　finally　by　comparing　the　accuracy　and　complexity　of　the　two　methods,　we　proposed　a　method　constitution　identification　of　TCM　which　is　based　on　tongue　images.　The　data　set　used　in　the　experiment　is　provided　and　acquired　by　the　Department　of　TCM　in　the　hospital　of　Beijing　University　of　Technology.　In　this　paper,　the　accuracy　of　the　three　types　of　physique　classification　of　Qi　deficiency,　damp　heat　and　phlegm　dampness　is　63%,　and　the　accuracy　of　traditional　machine　learning　algorithm　is　respectively　30%,56%　and　66%.　It　is　of　great　significance　to　clinical,　teaching　and　scientific　research　of　TCM　by　making　the　most　of　the　deep　learning　network　and　the　auxiliary　identification　of　physique.　©　2018　IEEE.