Extracting micromotion features from human radar echo data is a key to human target classification. Aimed at the problem that the traditional spectrum structure is hard to realize the fine recognition of similar body size, a method of human body identity authentication based on stack sparse autoencoder is proposed. First of all, this paper constructs a stacksparse selfencoder network, performs unsupervised pretraining by using human micromotion data, and extracts human micromotion features at different layers. Then the paper inputs the features into the softmax classifier for supervised training, and adjusts the network parameters by crossvalidation. Finally, the paper uses the trained network for human target classification. The average recognition rate of 3 people on the measured data set of different people reaches 83%, and is better than that by the method of extracting spectral structure feature classification.