Aerodynamic parameter identification methods can obtain accurate aerodynamic parameters and flight dynamics models, which are widely applied in aircraft development. The Maximum Likelihood method is frequently used for aerodynamic parameter identification in engineering fields, with its core ad-vantage being the likelihood criterion function, which can provide ideal identi-fication results. However, it requires an accurate flight dynamics model and initial values for the parameters to be identified. Genetic Algorithm excels in global optimization of nonlinear complex systems, and its advantages in parallel processing capabilities and high stability make it suitable for application in aer-odynamic parameter identification. Back-propagation Neural Network (BPNN) demonstrates strong capabilities in nonlinear modeling and data fitting, performing outstandingly and applicable to nonlinear aerodynamic parameter identification. Based on the strengths of these algorithms, two aerodynamic parameter identification algorithms were constructed: one based on the BPNN algorithm and another based on the Genetic-Maximum Likelihood Algorithm. Using test flight data from an aircraft, through experimental data analysis and pre-processing, sensitivity analysis of parameters, the aerodynamic parameters to be identified were determined. Reasonable aerodynamic models and flight dynamics models were built. Under typical “3211” longitudinal input excitation, longi-tudinal aerodynamic parameter identification was conducted. The results indicate that the Genetic-Maximum Likelihood identification algorithm can effectively obtain reliable aerodynamic parameters, with a maximum relative error of 5.87% compared to the design values and a good agreement compared to the experimental data. Due to the structure and algorithm design of the BPNN, the maximum relative error of the state variables obtained by the BPNN identification algorithm is 7.76%. Both algorithms can achieve satisfactory identification results and can be further extended and applied to the development of aircraft.