The observation of transmission busbar contact status of airport baggage transfer device is of great significance to reduce the unplanned stoppage of airport baggage transfer device and ensure the normal operation of airport. The temperature change can visually reflect the status of the transmission busbar contact, which is often accompanied by the rise of temperature when the transmission busbar contact failure occurs. Therefore, Encoder-Decoder LSTM can be used to predict the temperature of transmission bus contacts. First, an encoder composed of a bi-directional long and short-term memory network (Bi-LSTM) is used to encode the historical temperature data of the busbar contacts, then a decoder composed of a long and short-term memory network (LSTM) is used to predict the temperature value of the transmission busbar contacts for a future period. One month of temperature observation data of a domestic airport baggage conveyor is tested. The experimental results show that the time series prediction method using Encoder-Decoder LSTM outperforms the traditional time series prediction model as well as other existing deep learning prediction models.