Cable-stayed　bridges　are　one　of　the　most　popular　bridge　structures　for　mega　bridge　projects.　In　the　safe　operation　of　cable-stayed　bridge,　it　is　necessary　to　accurately　evaluate　the　state　of　the　bridge　structure　and　improve　the　accuracy　of　cable　damage　identification.　In　this　paper,　the　combined　damage　index　of　frequency　and　total　energy　change　rate　is　taken　as　input　vector.　Convolutional　Neural　Network　(CNN)　has　the　advantage　of　high-dimensional　feature　extraction.　Long　Short　Memory　Network　(LSTM)　advantage　in　time　series　modeling　ability.　A　combined　CNN&LSTM　method　was　proposed　to　recognize　the　cable-stay　damage　of　cable-stayed　bridges.　Then,　damage　identification　was　performed　on　the　cable-stayed　bridge　model　under　single　and　two　cable　damage　conditions.　And　varied　damage　conditions　are　selected　to　verify　the　accuracy　of　the　combined　CNN&LSTM　model.　The　results　show　that　the　damage　location　and　damage　degree　of　cable-stayed　Bridges　can　be　identified　with　high　precision　by　using　the　combined　CNN&LSTM　model.　©　2025　Ernst　&　Sohn　GmbH.