This study conducts a comparative analysis of intelligent methods for aerodynamic modeling of fixed-wing aircraft. Flight data underwent initial filtering to ensure subsequent simulation accuracy. An aerodynamic model was then established, utilizing inputs including angle of attack and elevator deflection. Lift coefficient was fitted using three intelligent methods: Support Vector Machines (SVM), Backpropagation (BP) neural networks, and Long Short-Term Memory (LSTM) networks. Comprehensive analysis and simulation demonstrate that the LSTM network achieved the most stable fitting performance, attaining the highest accuracy.