To enhance the precision of aircraft maneuver recognition method and improve the real-time of the recognition process, considering the dynamic and temporal nature of tactical maneuver trajectory, through fusing maneuver trajectory segmentation point detection method, an online maneuver trajectory recognition method based on the Mahalanobis distance-based dynamic time warping network is proposed. Firstly, in order to prevent the trained segmentation recognition model from overfitting, the flight parameters of the maneuver trajectory are converted into the maneuver trajectory feature parameters by extracting the maneuver trajectory features, and the maneuver library including 21 maneuver trajectory units is constructed. Secondly, in order to quickly split the maneuver trajectory units, a method is introduced that combines support vector machines and Mahalanobis distance:a maneuver trajectory segmentation point detection method based on Mahalanobis Distance Support Vector Machine. Then, in order to improve the accuracy of maneuver trajectory unit recognition, a method is proposed that combines dynamic time warping based on improved Mahalanobis distance and convolutional neural networks:a maneuver trajectory unit recognition method based on Mahalanobis distance measurement in dynamic time warping neural networks. Finally, by fusing the segmentation point detection model and the trajectory unit recognition model, an online maneuvering trajectory recognition platform is constructed, and the simulation analysis using three maneuver trajectory conditions data is carried out. The experimental results show that the proposed method can detect the segmentation point of the aircraft unit in real time, and the accuracy of the segmentation detection can reach 97.0%. Compared with other maneuver trajectory recognition methods, the proposed method not only meets real-time requirements, but also has high recognition accuracy exceeding 90%. The results validate the effectiveness and real-time performance of the online maneuver recognition model proposed in this research.