Abstract: The acquisition of flight loads has an important impact on aircraft design, aircraft reliability assessment, and aircraft life monitoring. In order to obtain high accuracy flight load, this paper establishes PCA-GA-BP neural network through principal component analysis and genetic algorithm-optimized BP neural network. In this paper, the flight parameters of an aircraft flight are taken as the input and the flight load is taken as the output. The PCA-GA-BP neural network is trained and predicted, and the prediction results are compared with those of the traditional BP neural network and PCA-BP neural network. The results show that the PCA-GA-BP neural network has the highest prediction accuracy and the smallest error fluctuation, with an average relative error of 5.79% and a minimum relative error of 0.07%. In summary, the PCA-GA-BP neural network is an excellent model for predicting flight loads because of its high prediction accuracy and extremely fast network convergence.