Due　to　the　increasing　importance　of　online　product　reviews,　how　to　accurately　identify　fake　reviews　has　become　an　issue　of　concern　to　enterprises　and　consumers.　The　contextual　features　encapsulate　the　semantic　information　of　review,　while　the　behavioral　features　reflect　the　behavioral　patterns　of　reviewers.　However,　an　appropriate　method　to　integrate　contextual　and　behavioral　features　is　a　challenging　task,　hence　an　end-to-end　model　based　on　Weighted　Fusion　of　Contextual　Features　and　Reviewer　Behaviors　(WF-CFRB)　for　fake　review　detection　is　proposed.　Firstly,　the　categories　of　average　cosine　similarity　and　the　corpus　of　review　are　jointly　fed　into　BERT　to　obtain　contextual　feature　vectors.　Then,　the　underlying　patterns　of　the　reviewer　behaviors　are　extracted　by　CNN　to　construct　behavioral　feature　vectors.　Finally,　a　weighted　fusion　method　is　adopted　to　fuse　contextual　and　behavior　features　for　fake　review　detection.　WF-CFRB　and　each　component　are　evaluated　on　YELP　dataset.　WF-CFRB　achieves　F1　score　of　81.31%　and　AUC　score　of　81.27%,　and　it　also　outperforms　the　other　baseline　models　in　terms　of　accuracy　and　recall.　Compared　with　the　original　BERT　model,　the　experimental　results　indicate　that　cosine　similarity　provides　BERT　with　more　information,　which　is　useful　to　construct　the　contextual　feature　vectors.　Through　the　weighted　fusion　of　contextual　and　behavioral　features,　WF-CFRB　yields　excellent　performance　on　fake　review　detection,　which　is　particularly　suitable　for　scenarios　where　behavioral　features　can　be　captured.