In order to solve the problem that traditional radar breathing identification relies on artificial predefined features, an ultrawideband (UWB) radar identification algorithm based on breath sample space (BSS) is proposed. The algorithm uses singular value decomposition (SVD) to filter out the clutter in the UWB radar human respiratory echo; the target cross-range respiratory signal is constructed as a BSS sequence containing time-distance information according to the echo; the convolutional neural network (CNN) is used to model the BSS to obtain the target classification results. In the indoor scene experiment, the identification accuracy of the four persons was 84. 64%. The comparison results show that the proposed algorithm has a good ability to distinguish the unique breathing characteristics of different individuals.