The atomic interferometer gyroscope (AIG), as one of the next-generation ultra-high-precision inertial sensor solutions, holds significant application value and potential development in defense and fundamental scientific research. However, its complex noise characteristics severely limit its actual performance. To address this critical issue, this article proposes a noise suppression method combining an ARMA model based on time series analysis with the Kalman filtering, aiming to bypass the complex physical modeling of noise sources and directly perform holistic modeling and filtering on the gyroscope′s output signal. First, the gyroscope output data are preprocessed via first-order differencing to meet the stationarity requirement of the ARMA model. The optimal ARMA (2,1) model parameters are determined through calculation and comparison using the AIC and BIC criteria. On this basis, an adaptive Kalman filtering algorithm for measurement noise is designed, which dynamically adjusts the noise covariance matrix by estimating the measurement noise variance in real time, effectively overcoming the parameter rigidity issue of traditional fixed-parameter filters. Experimental results from processing and analyzing 13 hours of atomic interferometer gyroscope output data demonstrate that the proposed adaptive Kalman filtering significantly enhances gyroscope performance. The bias stability improves from 0.076 6°/h to 0.055 0°/h (a 28.2% enhancement), the short-term sensitivity is optimized by 26.7%, and the long-term stability is improved by 20.1%. These improvements are notably superior to those of fixed-parameter filtering (only an 8% improvement). Furthermore, compared with non-model-based filtering methods (such as low-pass filtering and wavelet denoising), the adaptive Kalman filter exhibits superior noise suppression under model-matching conditions. The proposed method provides a practical and effective technical solution to overcome the challenges of complex noise modeling in atomic interferometer gyroscopes and enhance their real-world application performance.