In order to solve the difficult problem of online prediction of the remaining life of high-temperature and high-pressure vessel, a method of constructing the remaining life prediction model of high-temperature and high-pressure vessel based on digital twin was proposed. The method was based on real-time working conditions, using ANSYS simulation model for coupled simulation, obtaining a certain time-domain physical field of high-temperature and high-pressure vessel, establishing a sample dataset of remaining life prediction of high-temperature and high-pressure vessel through the multiaxial creep damage model, and using BP (back propagation) neural network algorithm optimized by Tent-SSA for training prediction, to establish a digital twin high-temperature and high-pressure vessel life prediction model driven by the fusion of the mechanism model and machine learning. Finally, the tube plate, which is a key component of a certain sodium-cooled fast reactor steam generator, was used as an object, and the experimental results show that the overall mean square error of the prediction model is reduced from 3.219 7 × 10^-2 before optimization to 7.744 9 × 10^-3, and the model is more stable, robust, and fast converging.