Aiming at the problem that the temperature and humidity data of medicines in medical cold chain system are not easy to diagnose, an improved long shortterm memory（LSTM） method for predicting the temperature and humidity of drugs is proposed. The method first expands the humidity data set by interpolation expansion algorithm, and then proposes an LSTM structure containing multiple LSTM cell elements instead of the traditional iterative prediction. Then the Adam optimization algorithm adjusts the network parameters and changes the network layer to reduce the prediction error. Achieve early prediction of the temperature and humidity of the drug. Finally, the test was carried out on the temperature and humidity data set of the drug collected in the pharmacy refrigerator. The mean square error was 0036 9. Compared with the traditional BP neural network prediction method and the Gaussian process mixture model prediction method, the improved LSTM drug temperature and humidity prediction method is more accurate.