针对传统轨迹相似性测度方法因仅考虑轨迹间距离而导致计算效率低、分类准确性不足的问题，提出一种基于长短期记忆网络（long short-term memory, LSTM）自编码器的多维特征融合模型。通过从原始飞行轨迹中提取位置特征（经度、纬度、高度）、形状特征（航向、曲率）及运动特征（速度、加速度），并采用最大-最小归一化进行特征融合，模型将高维轨迹数据编码至二维嵌入空间，以欧氏距离度量相似性。实验以上海浦东国际机场1 291条进场ADS-B轨迹为数据集，对比传统方法及深度学习方法（Transformer、图卷积网络）。结果表明：所提模型在轨迹分类准确率（94.7%）和DBI （Davies-Bouldin index）（0.33）上均显著优于对比方法，且计算耗时从传统方法的30 min缩短至10 min。该模型通过融合多维动态特征与高效降维机制，为空管系统的实时轨迹分析与异常检测提供了新思路。

A multidimensional feature fusion model based on a long short-term memory (LSTM) autoencoder was proposed to address the limitations of traditional trajectory similarity measurement methods, which suffered from low computational efficiency and insufficient classification accuracy due to considering only inter-trajectory distance. Positional features (longitude, latitude, altitude), shape features (heading, curvature), and motion features (speed, acceleration) were extracted from the original flight trajectories and fused using min-max normalization. The high-dimensional trajectory data were encoded into a two-dimensional embedding space, where similarity was measured using Euclidean distance. Experiments were conducted on a dataset of 1 291 arrival ADS-B trajectories at Shanghai Pudong International Airport, comparing traditional methods and deep learning approaches (Transformer, graph convolutional network). The results demonstrate that the proposed model significantly outperforms the comparative methods in trajectory classification accuracy (94.7%) and Davies-Bouldin index (DBI) index (0.33), while reducing computational time from 30 minutes to 10 minutes. By integrating multidimensional dynamic features with an efficient dimensionality reduction mechanism, the model provides a novel solution for real-time trajectory analysis and anomaly detection in air traffic management systems.