Cavity Ring-Down Spectroscopy (CRDS) is a highly sensitive trace gas concentration measurement technique in which the processing of ring-down time is crucial. This paper adopts the Kalman filter to process the cavity ring-down spectroscopy to reduce the measurement error introduced by noise during the collection and real-time measurement process. This method preprocesses with the traditional filtering method to obtain the observation noise covariance $\sigma v2(R)$ of the Kalman filter parameters, adjusts the process excitation noise covariance $\sigma W2(Q)$, and evaluates the filtering effect to optimize the measurement results. Using simulated ring-down signals with white noise, the linear regression summation method (LRS) fits the background rendering-downtimesto perform Kalman filtering. From four aspects of mean, standard deviation, residual standard deviation (RMSE), and different noise levels, the appropriate Q value range is obtained, which is less than 1×10^-7 and 0.001, respectively. An experimental gas detection system based on CRDS technology is constructed, using a 405 nm center wavelength diode laser and a high-reflectivity mirror with a reflectivity of over 99.99%, with NO₂ as the target gas, and the background ring-down time and ring-down time are processed and analyzed using Kalman filtering. The experimental results show that: (1) Selecting a Q value less than 1×10^-7 for Kalman filtering of the background ring-down time increases the lowest detection limit by 9.12 times and reaches 4.9×10^-11 after filtering; (2) Taking Q value of 0.001 for processing the ring-down time retains the time response information and achieves significant noise reduction; (3) The system’s time resolution is 1 s, and compared to the method of reducing time resolution to improve detection limit in the past, the Kalman filtering method improves the system’s sensitivity. The agreement between experimental and simulated results verifies the effectiveness of Kalman filtering in stability and noise reduction. Applying the Kalman filtering method in the CRDS spectroscopic detection of gases is practical and provides methods and references for optimizing other gas measurement results.