The　study　of　pornographic　image　recognition　plays　an　important　role　in　protecting　physical　and　mental　health　of　young　people.　However,　features　extracted　by　traditional　methods　are　so　single　that　these　methods　are　difficult　to　deal　with　the　diversity　of　pornographic　contents.　Therefore,　we　propose　a　deep　convolution　neural　network　(CNN)　for　pornographic　image　recognition　based　on　feature　visualization　analysis.　In　this　paper,　firstly　we　establish　a　pornographic　database　containing　80000　images.　Then,　we　train　a　basic　CNN　model　that　can　accurately　recognize　pornographic　images.　To　face　a　variety　of　challenging　scenes,　we　utilize　a　method　based　on　a　deconvolution　network　to　visualize　CNN　extractive　features.　By　using　this　visualization　method,　we　make　a　system　analysis　of　our　recognition　model　performance,　and　optimize　the　model　to　make　it　more　robust　and　accurate.　Our　experimental　results　suggest　that　the　proposed　method　significantly　outperforms　the　state-of-art　methods.　©　2018　IEEE.