To address the issue of low accuracy in random dynamic load identification, the atrous convolutional neural network (ACNN) is introduced into the research of random dynamic load identification. A random dynamic load

identification method based on one-dimensional ACNN with a dilation rate of 2 is proposed, which increases the

signal receptive field by increasing the dilation rate, thereby improving the accuracy of load identification. The

load identification verification experiment is conducted using the GARTEUR aircraft model as the research object, and the results show that the root mean square error between the random dynamic load on the wingtip excitation point identified by the method in this article and the real load is 0.990 1 N, with a correlation coefficient

of 0.987 4, and the power spectral density (PSD) curve can match well. Under different levels of noise interference, the proposed method can effectively identify the time series of random dynamic load. The proposed

method has the advantages of high recognition accuracy and strong anti-interference ability.

load identification, atrous convolutional neural network, dilation rate, random dynamic load