Abstract: here are security risks of important military facilities, transportation hubs, security agencies and other places. Using radar to collect human body echo signals for feature extraction and target classification is an efficient method to ensure the safety of these places. There is less data on human radar echoes that people can acquire in a limited time. In the case of smaller data samples, the problem of over-fitting is easy in the process of model training. And this will lead to the accuracy of the target classification decreased. In this paper, a method of identity authentication based on convolution neural network and micromotion feature is proposed. We use the idea of transfer learning. First, the convolution neural network classification model is preliminarily trained with the MNIST data set. We make the model have the ability of abstract the features and enter the model of human micro-motion samples to obtain the feature. And then model classifier is further trained by the human body micro-motion data. Finally, we get the training sample test the accuracy of the model. The experimental results show that the accuracy of this method is greatly improved compared with other approaches.