The attribute parameters of the radar attribute scattering center model can provide richer and more important information about the target,and the attribute scattering center parameter estimation is of great research significance for resolving radar targets.Aiming at the radar attribute scattering center model,this paper proposes the technique of fast target classification and parameter estimation of radar attribute scattering center based on deep learning.Firstly,the vision transformer(ViT)deep learning network is used to classify the radar attribute scattering centers into two categories:Localized and distributed,Then a convolution neural network for parameter estimation of attribute scattering centers(ASCNN)is constructed based on TS2Vec framework,and finally the two kinds of data are trained separately for parameter estimation of localized and distributed attribute scattering centers.Based on the attribute scattering center model,numerical experiments are carried out,the experimental results show that the accuracy of this method for radar attribute scattering center target classification is over 99%.The speed of radar attribute scattering center parameter estimation is more than 10000 times higher than that of traditional methods,and the accuracy is higher,which verifies the effectiveness and superiority of the proposed method.