Accurately identifying the peak value of impact load acting on the helicopter structure during weapon launch is of great significance to the design and finalization of weapon pylons. Firstly, a method of standardized preprocessing load signal is proposed by analyzing the vibration response and the characteristics of the impact load. Then, the test model of the weapon pylon is designed, and the position of the strain gauge is determined; the static load calibration test and the ground impact test are carried out on the test model. Next, the time-domain response measured by the strain gauge is filtered and de-noised. Impact load is processed by a standardized method. The response and load are used to train BP neural network and the mapping relationship between response and load is established. The impact load generated by a specific weapon is statistically processed to obtain the normalized average load time history, and the identified standard load is converted back to the original load pattern. Finally, the network that meets the error requirements is tested. Both the standardized pattern and the original pattern have high identification accuracy, which shows that an effective load identification model can be established based on the time-domain response signal and the standardized processed load signal.