The　key　to　damage　pattern　recognition　lies　in　digging　and　classifying　damage　features　from　the　response　data　of　civil　structures.　To　this　end，a　stack　auto-encoder　network　with　several　auto-encoder　hidden　layers　and　a　Softmax　classification　layer　is　built　for　analyzing　frame　structures.　A　hybrid　learning　mechanism　is　adopted　to　combining　unsupervised　and　supervised　learning　strategies.　Finite　element　analysis　is　used　to　generate　the　transmissibility　function　samples　corresponding　to　different　scenarios　of　a　frame　structure.　The　transmissibility　samples　are　then　divided　into　training，validation，and　test　sets.　The　parameters　of　the　auto-encoder　hidden　layers，such　as　the　weights　and　bias，are　determined　by　a　pre-training　strategy　in　order　to　avoid　the　phenomenon　of　network　over　fitting.　A　fine-tuning　step　is　employed　to　adjust　the　pre-trained　network　parameters，and　the　network　hyper　parameters　are　further　adjusted　based　on　the　validation　set.　The　measured　transmissibility　data　are　input　into　the　network　to　evaluate　the　damage　of　the　frame　structure.　The　analysis　results　show　that　the　proposed　method　can　effectively　extract　and　classify　the　damage　features.　Both　the　single　and　double　damage　scenarios　at　the　frame　joints　were　identified　with　higher　accuracy　and　better　anti-noise　ability　than　the　traditional　shallow　neural　network.　©　2024　Nanjing　University　of　Aeronautics　an　Astronautics.　All　rights　reserved.