A digital signal spectrum analysis system design based on polyphase decimation filtering and parallel FFT architecture is proposed to address challenges in ground-based microwave detection systems, such as weak ozone absorption peaks easily masked by noise and limitations in data input rates versus processing speeds in backend digital spectrometers. The method employs polyphase decomposition for input data decimation filtering, followed by parallel FFT processing on multi-channel data to derive the signal power spectrum. Its performance was validated through simulations and experimental atmospheric detection on a dedicated testbed. Results demonstrate that, within a reasonable observation period, this approach significantly suppresses noise, achieving a mean channel sensitivity of 2.77 K with an inter-channel sensitivity standard deviation of 0.2 K, reflecting the system response function′s stability. By maintaining spectral resolution while ensuring exceptional sensitivity, the proposed spectrometer meets practical detection system requirements.