The usage of mmWave radar for non-contact vital signs monitoring has shown great potentials in the medical and healthcare fields, which enables continuous and imperceptible identity verification. Due to the complex impact of various factors on heart movement, the FMCW mmWave radar can better monitor and capture heart data during sleep, and the obtained heart data can be recognized and classified based on the uniqueness of personal heart movement characteristics. In this study, we propose a deep convolution neural network for identification recognition from one-dimensional time series data of the heart radar signal. The results were compared with 3 SOTA methods, i.e. LSTM, InceptionTime and LSTformer. All the models achieved classification accuracies about 90% on an experimentally acquired heart signal data set in sleep posture. The InceptionTime model has the highest accuracy, but it takes the longest time. The LSTM and LSTformer models have the lower accuracy but the shorter calculation time. The proposed CNN model can obtain similar accuracy but better efficiency in comparison with InceptionTime model.