Aimed at the problems that the traditional ballistic targets micromotion classification is lack of intelligence and the classification performance is poor under noise conditions , by using the highdimensional feature generalization learning ability of deep learning, a method of using deep convolution neural network for ballistic target micromotion classification is proposed. Firstly, based on the establishment of the ballistic target micromotion model, the microDoppler representations of the three micromotion forms are analyzed, and the timefrequency map of the radar echo signals is generated as the data set for training, verification and testing; The transfer learning in deep convolution neural network is used to retrain AlexNet and GoogLeNet. Finally, the target network classification in three micromotion forms is realized by using the trained network, and the influence of signaltonoise ratio on classification performance is studied. The simulation results show that compared with the traditional micromotion target classification method, the method is not only high in intelligence, but also is good in classification accuracy under low SNR conditions, and is guidable in the classification of ballistic targets.