随着中国民航的飞速发展，终端区空中交通流量与日俱增，短时空中交通流量预测对于精准实施空中交通流量管理具有重要意义。为提高短时空中交通流量预测的准确性，提出了基于数据差分处理（data differential processing）的经验模态分解（empirical mode decomposition, EMD）和长短期记忆（long short-term memory, LSTM）相结合的短时空中交通流量预测模型。首先，该模型对短时空中交通流量序列进行经验模态分解；其次，为了提高预测精度，运用数据差分对时间序列进行平稳化处理；最后，将平稳处理后的序列分别输入LSTM网络模型进行预测，经过数据重构，得到最终的短时流量预测值。利用郑州新郑国际机场数据进行了实验验证，结果表明，该模型预测精度和拟合程度的典型指标RSME、MAE、R²分别为0.29%，0.08%、96.40%，相较于其他方法，预测精度大幅度提高，可以为短时空中交通流量预测提供有益参考。

With the rapid development of China's civil aviation, the air traffic flow in terminal areas is experiencing a consistent and significant increase. The accurate forecast of short-term air traffic flow is of great significance for the efficient implementation of air traffic flow management. To enhance the accuracy of short-term air traffic flow forecast, a model combining EMD (empirical mode decomposition) and LSTM (long short-term memory) based on data differential processing was proposed. Firstly, the model performed empirical mode decomposition on short-term air traffic flow sequences. Secondly, to improve prediction accuracy, data difference was utilized to stabilize the time series. Finally, the processed sequences were input into the LSTM network model for prediction, and the final short-term traffic prediction value was obtained through data reconstruction. Experimental verification was conducted using the data from Zhengzhou Xinzheng International Airport. The results demonstrate that the model achieves a significant improvement in prediction accuracy, as indicated by the typical indexes RSME, MAE, and R², which are 0.29, 0.08, and 96.40%, respectively. This approach outperforms other methods and provides valuable reference for short-term air traffic flow prediction.