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In order to solve the handicap that the performance parameters of auxiliary power unit (APU) are hard to accurately forecast, the study proposed a hybrid model of dual-stage attention mechanism (DAM) and CNN (convolutional neural network)-LSTM (long short-term memory) based on characteristic and temporal. The method introduced a channel attention mechanism in the characteristic extraction phase and a temporal attention mechanism in the output phase. Enhanced the CNN"s ability to extract important characteristics. The influence of key information from historical moments on the prediction output was enhanced simultaneously. For the purpose of improving the prediction accuracy, the improved pso algorithm was used to optimize the hyperparameters of the LSTM network. The proposed new method has extremely low errors in the prediction of APU exhaust gas temperature (EGT) with multi-variable input and multi-step length, and the experimental results show that the prediction accuracy is significantly improved.