To　improve　the　deficiencies　of　the　current　traditional　damage　detection　methods,　such　as　the　inadequate　detection　ability　for　local　damage　and　minor　damage,　the　convolution　neural　network　based　on　deep　learning　method　is　proposed　to　improve　statistical　damage　pattern　recognition　for　bridge.　According　to　the　demand　of　the　damage　characteristic　vectors　of　the　convolutional　neural　network,　the　original　structural　responses　of　vehicle-bridge　coupling　vibration　are　regenerated　by　wavelet　packet　filtering　and　reconstruction,　and　the　recurrence　plots　of　different　damage　cases　are　obtained　as　the　damage　characteristics　images　and　the　input　of　convolution　neural　network　according　to　recurrence　analysis.　On　this　basis,　the　corresponding　calculation　process　and　method　for　damage　detection　of　bridge　structure　based　on　convolution　neural　network　and　recurrence　plot　are　established.The　damage　simulation　for　a　continuous　beam　bridge　is　carried　out,　including　damage　locations　and　degrees.　The　damage　feature　vectors　such　as　frequency　band　energy　of　wavelet　packet　and　recurrence　plots　are　extracted,　and　damage　detection　based　on　multiple　statistical　pattern　recognition　algorithm　is　carried　out.　The　results　showed　that　the　damage　information　in　recurrence　plots　is　more　abundant　compared　with　other　feature　vector,　and　the　convolutional　neural　network　can　fulfill　automatic　extraction　and　distinguishing　feature　more　accurately　according　to　layer-by-layer　intelligent　learning,　compared　with　the　traditional　damage　detection　methods,　such　as　support　vector　machine　and　BP　neural　network.　Hence,　the　damage　detection　method　based　on　convolution　neural　network　and　recurrence　plot　can　achieve　more　identification　accuracy　for　damage　location　and　degree.　©　2020,　The　Editorial　Board　of　Journal　of　Basic　Science　and　Engineering.　All　right　reserved.