In response to the shortcomings and deficiencies of the improved back propagation network in aviation equipment fault diagnosis, a hybrid algorithm is formed by combining the adaptive genetic algorithm and the improved back propagation algorithm to train an artificial neural network. Taking the improvement of the initial weight space of the back propagation network as the starting point, a multi-point adaptive genetic optimization is carried out using the improved genetic operation. Based on this, the improved back propagation algorithm is used to carry out local precise search and ultimately achieve global optimization. Taking the fault diagnosis of a certain aircraft electrical control box and a certain aircraft autopilot flight control box as examples, the proposed algorithm was simulated and studied. The simulation results showed that the combination of adaptive genetic algorithm and improved back propagation algorithm has fast convergence speed and high diagnostic accuracy, and has good diagnostic results for engineering samples with complex input-output relationship.