Steel　strands　serve　as　the　key　load-bearing　components　of　pre-stressed　bridges,　yet　the　identification　of　effective　pre-stress　for　steel　strands　is　a　challenging　task.　In　this　study,　a　time　and　frequency　adaptive　fusion　input-one　dimensional　convolutional　neural　network-long　short-term　memory　(TFAFI-1DCNN-LSTM)　framework　was　proposed　for　ultrasonic　guided　wave　(UGW)based　effective　pre-stress　identification　of　steel　strands.　In　this　framework,　the　time　and　frequency　domain　signals　of　UGW　were　input　into　the　network　as　two　parallel　branches,　CNN　and　LSTM　were　utilized　to　extract　spatial　and　serial-related　features,　and　a　trainable　weight　coefficient　was　introduced　for　adaptive　fusion　of　time　and　frequency　domain　features.　Firstly,　ablation　studies　were　conducted　to　investigate　the　influence　of　each　key　component　in　the　proposed　framework　on　the　prediction　results.　Secondly,　deep　learning　(DL)　models　of　Res　Net,　Dense　Net　and　Inception　Net　were　introduced　as　comparisons,　and　the　prediction　results　based　on　TFAFI-1DCNN-LSTM　were　compared　with　those　based　on　the　above　DL　models.　Thirdly,　the　noise-robustness　and　performance　under　a　few　trainable　samples　of　TFAFI-1DCNN-LSTM　were　also　investigated.　Finally,　the　outputs　of　various　key　layers　of　the　proposed　framework　were　subjected　to　dimensionality　reduction　and　visualization　to　provide　an　intuitive　demonstration　of　the　feature　extraction　process,　and　the　inherent　mechanisms　of　the　proposed　framework　were　interpreted　using　local　interpretable　model-agnostic　explanations　(LIME).　Results　show　that　the　architecture　of　TFAFI-1DCNN-LSTM　is　reasonably　designed　and　all　key　components　are　necessary.　The　weights　of　features　in　time　and　frequency　domain　branches　are　rationally　assigned　due　to　the　addition　of　an　adaptive　weight　coefficient.　Compared　with　other　DL　models,　the　proposed　TFAFI-1DCNNLSTM　exhibits　higher　pre-stress　identification　accuracy,　excellent　noise-robustness,　and　superior　performance　under　only　a　few　trainable　samples.　The　correlation　between　features　extracted　by　the　network　and　the　pre-stress　labels　significantly　increases　with　the　deepening　of　the　network,　and　the　correlation　between　input　UGW　signals　and　predicted　pre-stress　values　is　effectively　interpreted　utilizing　LIME,　proving　that　the　architecture　of　the　proposed　framework　is　reasonable　and　effective,　the　predicted　results　are　reliable　and　credible.