The prediction accuracy of the Remaining Useful Life (RUL) is of vital importance to rejuvenation decision of Web-based software systems, so we propose a real-time prediction method for the remaining useful life of Web-based software systems based on the Long Short-Term Memory (LSTM) network. Firstly, an accelerated life test platform is built to collect the performance indicators of the aging of Web-based software systems. Then, according to the time-series characteristics of indicator data, an LSTM-based real-time prediction model for the remaining useful life of Web-based software systems is constructed and trained. The experimental results show that the model can effectively predict the remaining useful life of Web-based software systems in real time with higher accuracy and stronger applicability. This method provides technical support for optimizing system’s rejuvenation decision.