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In cut tobacco production line, the loosening and conditioning process is one of the most significant links affecting tobacco leaves quality. In order to solve the modeling difficulties of tobacco loosening and conditioning system due to time delay, strong coupling, nonlinearity and missing parameters, a data-driven model based on Long-Short-Term Memory networks is designed. Using the strong time series information learning ability and nonlinear fitting ability of the LSTM networks, it is trained only with the historical time series data of the outlet moisture and temperature of the loosening and conditioning cylinder, and the system model that can accurately predict the outlet moisture and temperature in output tobacco is obtained. The model predicts the output moisture and temperature values at the next time by inputting 60 consecutive historical output values. It is verified that the model has excellent fitting effect on both training set and verification set.