Production forecasting plays an important role in well management, optimizing production, and maximizing economic returns in shale gas development. Traditional rate-decline analysis and analytical models face limitations in forecasting shale gas production. Eight horizontal shale gas wells from North America were selected. The production data of these wells were processed into time series. A long short-term memory (LSTM) network combined with Bayesian optimization was applied to predict shale gas well flowback performance. Key parameters for production forecasting were identified. The results show that flowing bottomhole pressure and daily water rate are the most important input features. The prediction accuracy of the LSTM model generally decreases as the forecast period increases. These findings provide valuable insights into the application of time series analysis methods in production forecasting of shale gas wells.