[19] constructed an LSTM-based deep learning network to predict the temperature state of the integrated RF module. They fine-tuned the parameters to analyze the error curves between the predicted and observed values. In this research, the effects of the initial learning rate and max epochs on the training time and prediction accuracy are discussed. When the initial learning rate is 0.0001 and the max epochs is 60, the training time of the model is 3 min and 7 s, but the error does not tend to converge until after 50 epochs of training, and the final predicted value differs significantly from the actual value, with an RMSE of 0.00225. When the learning rate is increased to 0.0005 and the max epochs is 120, the training time of the model exceeds the original for 6 min and 34 s. However, the error converges sufficiently, and the accuracy of the predicted value can reach up to 98.7%, which also indicates that the initial learning rate and max epochs are important elements affecting the convergence of the error, the training time, and the prediction accuracy. [PERSON] et al. [20] built a self-supervised deep long short-term memory (SSDLSTM) network for real-time monitoring of pot temperature in electrolysis production in the aluminum processing industry with high accuracy and robustness. In addition, the prediction accuracy and training time of this model are more competitive than traditional LSTM models and other RNN models. [PERSON] et al. [21] built a model based on an LSTM deep learning network to predict the indoor temperatures of a building. Their results show that the modified model of LSTM is able to maintain the highest accuracy in both single time-step-ahead prediction and multiple time-step-ahead prediction compared to BPNN, SVW and DT, which also means that the modified LSTM model is more capable of reducing the relative error in the prediction of indoor temperatures than the traditional neural network model. [PERSON] et al. [22] developed four different types of LSTM models to predict the change in the temperature demand of air and fresh products when applying the demand response in a cold room. Their study shows that using a small amount of data can also maintain a low relative error in temperature prediction as well as reduce the time needed for training if the temporal characteristics of the dataset are regular enough. Meanwhile, the convolutional LSTM model and bidirectional LSTM model are more susceptible to noise than the traditional LSTM model and stacked LSTM model, which means that the reasonable location of the data collection will be important for the improvement of their prediction accuracy. The use of LSTM models in all the above studies has relatively promising results, and it can be used to predict the temperature variation in the temperature-controlled container with a cold energy storage system [23]. In summary, LSTM neural networks have been widely used for temperature prediction in the complex thermal environment. In this paper, a temperature prediction model based on an LSTM neural network is developed to predict and analyze the temperature variation in the temperature-controlled container. The results of this paper will be helpful for temperature control in the fresh-keeping environment with a cold energy storage system.