Traditional　statistical　pattern　identification　methods,　such　as　artificial　neural　network　and　support　vector　machine,　have　limited　ability　to　identify　minor　damage　of　bridges.　Deep　learning　can　mine　the　inherent　law　and　representation　level　of　sample　data.　As　a　typical　algorithm　of　deep　learning,　convolutional　neural　network　is　a　feedforward　neural　network　with　deep　structure　and　convolution　calculation,　and　its　ability　of　image　identification　is　very　outstanding.　The　recurrence　graph　of　structural　response　can　reveal　the　internal　structure,　similarity,　and　damage　information.　The　original　structure　response　signal　involves　the　coupling　vibration　of　vehicle　and　bridge　is　filtered　and　reconstructed　by　wavelet　packet,　and　then　the　recurrence　graph　of　different　damage　cases　is　obtained,　which　is　used　as　the　input　image　of　convolutional　neural　network　as　a　new　type　of　damage　feature;　thus,　a　damage　identification　method　based　on　convolutional　neural　network　and　recurrence　graph　is　established.　The　results　of　numerical　simulation　and　model　experiment　show　that　the　recurrence　graph　contains　more　damage　information;　compared　with　the　traditional　statistical　pattern　identification　methods,　convolutional　neural　network　can　achieve　more　accurate　feature　extraction　and　identification　through　intelligent　learning　layer　by　layer,　so　as　to　realize　more　accurate　identification　of　damage　location　and　damage　degree.