This　article　focuses　on　the　Assembled　Structure　(AS)　state　recognition　method　based　on　vibration　data.　The　difficulty　of　AS　state　recognition　is　mainly　the　extraction　of　effective　classification　features　and　pattern　classification.　This　paper　presents　an　integrated　method　based　on　Convolutional　Neural　Networks　(CNNs)　and　data　fusion　for　AS　state　recognition.　The　method　takes　the　wavelet　transform　time-frequency　images　of　the　denoised　vibration　signal　as　input,　uses　CNNs　to　supervise　and　learn　the　data,　extracts　the　deep　data　structure　layer　by　layer,　and　improves　the　classification　results　through　data　fusion　technology.　The　method　is　tested　on　an　assembly　concrete　shear　wall　using　shake-table　testing,　and　the　results　show　that　it　has　a　good　overall　identification　accuracy　(IA)　of　94.7%,　indicating　that　it　is　robust　and　capable　of　accurately　recognizing　very　small　changes　in　AS　state　recognition.　©　2023　by　the　authors.