Colonoscopy　is　an　effective　measure　for　detecting　early　colorectal　lesions.　To　obtain　high-quality　pictures　during　colonoscopy　examination,　the　bowel　preparation,　or　cleansing,　is　considered　as　the　first　and　critical　step.　To　date,　the　evaluation　of　bowel　preparation　quality　is　performed　by　colonoscopists　in　most　of　gastroenterology　departments.　This　manual　labeling　is　time-consuming,　uneconomical　and　sometimes　unreliable　subject　to　expertise　of　physicians.　In　this　work,　we　propose　an　automatic　evaluation　model　that　labels　the　cleanliness　for　bowel　preparation　in　accordance　with　the　Boston　Bowel　Preparation　Scale　(BBPS).　A　compact　convolutional　neural　network　(CNN)　with　2　Densenet　layers　which　have　feature　reuse　mechanism　embedded　before　the　softmax　classifier　is　utilized.　The　proposed　model　achieves　promising　evaluation　performance　on　our　dataset　as　well　as　on　a　public　dataset　named　Nerthus　where　is　better　than　the　existing　research　results.　The　average　correct　evaluation　ratio　is　up　to　89.58%　in　20　individual　cases　(573　images　in　total),　where　each　case　includes　16　images　at　least.　The　method　proposed　in　this　paper　is　considered　to　be　effective　and　feasible　by　professional　colonoscopists　and　can　be　used　for　clinical　auxiliary　diagnosis.　©　2019　IEEE.