The invention relates to a deep convolutional adversarial neural network-based human body action radar image classification method. The method comprises the steps of constructing a data set; realizingradar image data enhancement through a DCGAN: establishing the DCGAN, using the network to individually learn each radar spectrogram, generating new radar spectrograms according to characteristics learned by the network, expanding training set samples under the condition of a certain data amount, adjusting parameters by the network to ensure that fewest failure images are generated, and expandingthe data set to the maximum extent, thereby realizing the data enhancement; and extracting upper, middle and lower envelopes in each radar image to serve as eigenvectors, taking the three eigenvectors as inputs of a support vector machine classifier, and classifying radar image data by utilizing a support vector machine, wherein the upper and lower envelopes represent echo radial velocities of human limbs, the middle envelope represents an echo radial velocity of a human trunk.