Due　to　the　anonymous　and　free-for-all　characteristics　of　online　forums,　it　is　very　hard　for　human　beings　to　differentiate　deceptive　reviews　from　truthful　reviews.　This　paper　proposes　a　deep　learning　approach　for　text　representation　called　DC　Word　(Deep　Context　representation　by　Word　vectors)　to　deceptive　review　identification.　The　basic　idea　is　that　since　deceptive　reviews　and　truthful　reviews　are　composed　by　writers　without　and　with　real　experience　on　using　the　online　purchased　goods　or　services,　there　should　be　different　contextual　information　of　words　between　them.　Unlike　state-of-the-art　techniques　in　seeking　best　linguistic　features　for　representation,　we　use　word　vectors　to　characterize　contextual　information　of　words　in　deceptive　and　truthful　reviews　automatically.　The　average-pooling　strategy　(called　DC　Word-A)　and　max-pooling　strategy　(called　DC　Word-M)　are　used　to　produce　review　vectors　from　word　vectors.　Experimental　results　on　the　Spam　dataset　and　the　Deception　dataset　demonstrate　that　the　DCWord-M　representation　with　LR　(Logistic　Regression)　produces　the　best　performances　and　outperforms　state-of-the-art　techniques　on　deceptive　reviewidentification.　Moreover,　the　DC　Word-M　strategy　outperforms　the　DC　Word-A　strategy　in　review　representation　for　deceptive　review　identification.　The　outcome　of　this　study　provides　potential　implications　for　online　review　management　and　business　intelligence　of　deceptive　review　identification.