With　the　widespread　of　deceptive　opinions　in　the　Internet,　how　to　identify　online　deceptive　reviews　automatically　has　become　an　attractive　topic　in　research　field.　Traditional　methods　concentrate　on　extracting　different　features　from　online　reviews　and　training　machine　learning　classifiers　to　produce　models　to　decide　whether　an　incoming　review　is　deceptive　or　not.　This　paper　proposes　an　approach　called　DRI-RCNN　(Deceptive　Review　Identification　by　Recurrent　Convolutional　Neural　Network)　to　identify　deceptive　reviews　by　using　word　contexts　and　deep　learning.　The　basic　idea　is　that　since　deceptive　reviews　and　truthful　reviews　are　written　by　writers　without　and　with　real　experience　respectively,　the　writers　of　the　reviews　should　have　different　contextual　knowledge　on　their　target　objectives　under　description.　In　order　to　differentiate　the　deceptive　and　truthful　contextual　knowledge　embodied　in　the　online　reviews,　we　represent　each　word　in　a　review　with　six　components　as　a　recurrent　convolutional　vector.　The　first　and　second　components　are　two　numerical　word　vectors　derived　from　training　deceptive　and　truthful　reviews,　respectively.　The　third　and　fourth　components　are　left　neighboring　deceptive　and　truthful　context　vectors　derived　by　training　a　recurrent　convolutional　neural　network　on　context　vectors　and　word　vectors　of　left　words.　The　fifth　and　six　components　are　right　neighboring　deceptive　and　truthful　context　vectors　of　right　words.　Further,　we　employ　max-pooling　and　ReLU　(Rectified　Linear　Unit)　filter　to　transfer　recurrent　convolutional　vectors　of　words　in　a　review　to　a　review　vector　by　extracting　positive　maximum　feature　elements　in　recurrent　convolutional　vectors　of　words　in　the　review.　Experiment　results　on　the　spam　dataset　and　the　deception　dataset　demonstrate　that　the　proposed　DRI-RCNN　approach　outperforms　the　state-of-the-art　techniques　in　deceptive　review　identification.