Non-contact vital sign monitoring using millimeter-wave radar offers continuous and discreet identification. Cardiac motion is influenced by various complex factors, making it challenging to capture characteristic waveform information. To address this, the study employs millimeter-wave radar transmitting frequency modulated continuous waves to monitor and record cardiac data during sleep. Additionally, the paper proposes a deep convolutional neural network (CNN)-based identity recognition method using one-dimensional time-series radar signals of cardiac motion. The performance of this method is compared with three deep learning algorithms: long short-term memory Network, InceptionTime, and LSTformer. The final classification accuracies of all models exceed 85% on a dataset of heart signals collected in a resting state in the laboratory. Among the models, InceptionTime achieves the highest accuracy but requires the longest processing time. The long short-term memory and LSTformer models exhibit lower accuracy but faster processing. The CNN model proposed in this study demonstrates comparable accuracy to InceptionTime, while requiring less computational time, thus balancing accuracy and efficiency.