In order to predict the Remaining Useful Life (RUL) of Web software system in real time and accurately, taking into consideration the time sequence characteristics of the Web system health status performance indicators and the interdependence between the indicators, a real-time remaining life prediction method of Web software system based on Self-Attention-Long Short-Term Memory (Self-Attention-LSTM) network was proposed. Firstly, an accelerated life test platform was built to collect the performance indicators data reflecting the aging trend of the Web software system. Then, according to the time sequence characteristics of the performance indicators data, a Long Short-Term Memory (LSTM) recurrent neural network was constructed to extract the hidden layer characteristics of the performance indicators, and the self-attention mechanism was used to model the dependency relationship between the characteristics. Finally, the real-time RUL prediction value of the Web system was obtained. On three test sets, the proposed model was compared with the Back Propagation (BP) network and the conventional Recurrent Neural Network (RNN). Experimental results show that the Mean Absolute Error (MAE) of the model is 16.92% lower than that of LSTM on average, and the relative accuracy (Accuracy) of the model is 5.53% higher than that of LSTM on average, which verify the effectiveness of the RUL model of Self-Attention-LSTM network. It can be seen that the proposed method can provide technical support for optimizing the software rejuvenation decision of the Web system.