Aiming at the problems of insufficient identification accuracy and difficulty in collecting training samples in existing vehicle load identification method based on neural network, a vehicle load identification method based on time-frequency analysis of strain signal and Convolutional Neural Network (CNN) is proposed to identify the total weight of mobile vehicles. Firstly, the time-frequency characteristics of the strain signal are obtained by using the wavelet time-frequency transform, and the time-frequency matrix is changed into a 64×64 numerical matrix as the input data of CNN. Secondly, in order to realize the purpose of unknown vehicle load identification, the map? ping relationship between strain response and vehicle load is directly established after training a small number of nu? merical matrices by using the regression learning algorithm of CNN. Finally, through simulation tests, it is found that although the load recognition results of the CNN are affected to varying degrees under the influence of different road roughness and noise, the vehicle load can still be more accurately identified under the influence of certain road rough? ness and noise.