We propose a radar-based deep learning model for human activity classification. In this paper, for the first time, the radar spectrogram is treated as a time-sequential vector, and a DL model composed of 1-D convolutional neural networks (1D-CNNs) and recurrent neural networks (RNNs) is proposed. The experiment results show that the proposed model can not only achieve the highest accuracy but also have the fewest number of parameters than that of existing 2-D CNN methods.