15CrMo　steel　has　good　mechanical　properties　and　is　widely　used　in　high-temperature　pressure　components.　After　long　term　service,　it　is　easy　to　cause　material　spheroidisation.　The　metallographic　and　mechanical　property　tests　are　used　to　evaluate　the　spheroidisation　of　specimens.　Ultrasonic　backscattering　signal　is　sensitive　to　microstructure　and　contains　more　microstructure　information,　which　can　be　used　for　the　evaluation　of　spheroidisation.　However,　the　ultrasonic　backscattering　signal　is　nonlinear,　and　feature　extraction　is　difficult.　In　this　study,　ultrasonic　testing　is　used　to　scan　different　spheroidisation　specimens,　and　the　ultrasonic　backscattering　signal　is　extracted　as　the　input　to　the　deep　learning　model,　which　is　used　to　extract　features　from　the　backscattering　signal.　The　models　are　evaluated　using　classification　and　regression　evaluation　metrics.　The　results　show　that　the　proposed　CNN-LSTM　model　has　good　identification　performance　for　the　classification　of　spheroidisation　and　the　prediction　of　mechanical　properties.　The　classification　accuracy,　recall,　precision,　and　F1-score　are　all　1.　Additionally,　the　maximum　predicted　RMSE　and　MAE　values　are　only　2.33　MPa　and　1.70　MPa,　and　the　minimum　R2　is　only　0.97.　The　worst　prediction　is　the　tensile　strength,　with　an　average　value　of　442.2　MPa　and　a　maximum　value　of　481.1　MPa.