Abstract: In order to make full use of the time correlation of airport delay status information and improve the accuracy of airport delay prediction, an airport delay prediction method based on hybrid coding and Long Short Term Memory（LSTM） network is proposed. The method firstly preprocesses the airport's information, meteorological information and flight information to obtain the airport delay data. Then, the LSTM network is used to extract the feature of the airport delay data. Finally, the Softmax classifier is constructed to predict the classification of airport delays. The experiments results show that the hybrid coding method proposed in the data preprocessing stage based on airport delay data can improve the prediction accuracy by about 5%. Moreover, when the LSTM network is used to extract the time-related feature information of the data, the prediction accuracy of the LSTM network model is up to 94.01%. And the analysis of the universality of the network using different airport data shows that the algorithm is more suitable for medium and large hub airports with large amount of original data.