Compared with traditional inertial sensors and visual sensors, using millimeter-wave radar for human activity detection not only requires low environmental demands and high sensitivity but also can effectively address privacy leakage issues associated with cameras. To tackle the problems of clutter interference and high complexity of network models in current millimeter-wave radar human activity detection, this paper proposes improvements and quantization processing of existing residual neural network. By integrating radar time-frequency transformation and clutter suppression, a complete signal processing flow for radar human activity detection is presented. The time-frequency transformation section adopts range dimension FFT, high-pass filtering in slow-time dimension, and short-time Fourier transformation to obtain Time-Doppler spectrum. The residual network section embeds the CBAM attention mechanism and quant it from 32 bits to 8 bits. Finally, the Time-Doppler spectrums are input into the network model for feature extraction and classification to obtain detection results. Experimental results demonstrate that this method can eliminate interference from static clutter, achieving a detection accuracy of 97.33% with a model size of 20.2 MB.