The　accurate　assessment　of　the　effective　pre-stress　in　steel　strands　is　a　challenging　task,　and　ultrasonic　guided　wave　(UGW)　technique　has　shown　certain　application　prospects　in　this　field.　However,　the　existing　UGW-based　approaches　require　manual　parameter　extraction　from　signals　in　time　domain　or　frequency　domain,　which　is　a　cumbersome　and　time-consuming　process,　and　pre-stress　identification　based　on　individual　parameters　may　not　be　reasonable.　This　study　proposes　a　framework　for　identifying　effective　pre-stress　in　steel　strands　based　on　UGW　and　one-dimensional　convolutional　neural　network　(1D-CNN),　which　does　not　require　any　parameter　extraction　operation　and　achieves　high　identification　accuracy.　The　output　features　of　various　convolutional　layers　in　1D-CNN　were　downscaled　and　visualized,　and　the　prediction　results　of　1D-CNN　were　compared　with　those　of　a　support　vector　regression　(SVR)　model.　Results　show　that　with　the　deepening　of　the　network,　the　correlation　between　output　features　of　the　convolutional　layers　and　pre-stress　values　increases　significantly,　indicating　that　the　1D-CNN　model　is　able　to　automatically　extract　features　related　to　the　variation　of　pre-stress.　The　pre-stress　prediction　accuracy　using　1D-CNN　is　significantly　higher　than　that　using　SVR,　and　the　prediction　error　is　within　3%.　The　proposed　1D-CNN　model　exhibits　excellent　noise-robustness,　with　the　prediction　error　remaining　within　10%　even　at　the　SNR　level　of　-5　dB.　Even　after　removing　half　of　conditions　in　the　training　set,　the　proposed　1D-CNN　model　is　still　able　to　achieve　accurate　identification　of　effective　pre-stress.