为应对日益复杂多变的市场环境带来的航空货运市场供需不平衡问题，提高航空货运量预测精度对于航线规划和供应链优化等具有重要意义。首先基于2000年1月—2022年12月的月度航空货运数据作为训练集，通过季节性分解法（seasonal and trend decomposition using loess, STL）捕捉季节性波动规律和长期变化趋势，然后基于深度学习的时间序列预测模型（long short-term memory-support vector regression, LSTM-SVR）来拟合因突发事件下的货运量引起的非线性变化，最后基于2023年全年的月度数据对预测模型进行检验。结果表明：基于季节和组合预测模型（STL-SVR-LSTM）相比于传统方法如自回归移动平均（autoregressive integrated moving average, ARIMA）、SVR或LSTM在突发事件下对航空货运量的预测更为准确。2023年的数据检验得出季节和组合预测模型均方根误差和平均绝对百分比误差分别为3.53和3.53%，拟合优度为0.79，LSTM模型预测结果次优，均方根误差和平均绝对百分比误差分别为5.66和7.73%，拟合优度为0.58，显著优于其他两种传统预测模型。可见该预测模型能适应复杂环境下的航空货运量预测，有助于在突发事件下为企业经营和增强供应链的稳定提供参考建议。

In order to address the supply-demand imbalance in the increasingly complex and changing market environment, improving the accuracy of air cargo volume forecasting is of great significance for route planning and supply chain optimization. Firstly, based on monthly air cargo data from January 2000 to December 2022 as the training set, seasonal fluctuations and long-term trends were captured using seasonal and trend decomposition using loess (STL). Secondly, a deep learning time series prediction model (LSTM-SVR) was used to fit the nonlinear changes in cargo volume due to emergencies. Finally, the prediction model was tested based on monthly data for the entire year of 2023. The results indicate that the seasonal and combination prediction model (STL-SVR-LSTM) is more accurate in predicting air cargo volume during emergencies compared to traditional methods such as ARIMA, SVR, or LSTM. The data validation in 2023 shows that the root mean square error and average absolute percentage error of the seasonal and combination prediction models are 3.53 and 3.53%, respectively, with a goodness of fit score of 0.79. The LSTM model has the second best prediction results, with root mean square error and average absolute percentage error of 5.66 and 7.73%, respectively, and a goodness of fit score of 0.58, significantly better than the other two traditional models. It can be seen that this prediction model can adapt to the prediction of air cargo volume in complex environments, which is helpful in providing reference suggestions for enterprise operation and enhancing supply chain stability in case of emergencies.