Because of the problems that accuracy is low in recognizing complex flight action, and in order to enhance the accuracy and reliability of flight parameter data analysis, this paper proposes a flight action recognition method based on time convolutional network mapping anchor boxes. The method is to construct a FD̠Net recognition model by improving the YOLOv3 network structure, transforming the recognition of complex flight action into a problem of regional division and classification in the time di mension. A selection method of complex flight action with key feature parameters is proposed, and an input system with 25 feature parameters is constructed. A data augmentation method based on scale scaling is adopted by solving the problem of sample imbalance. Loss functions for prediction box regression, confidence regression, and classification regression are designed to complete model training. The experi mental results show that compared with the existing methods, the proposed method significantly im proves the accuracy of complex flight action recognition and significantly enhances computational efficien cy, and the effectiveness and practicality of the method are verified.