Aimed at the problem of data noise interference in bridge bearing tilt angle and stress monitoring, a real-time monitoring data preprocessing system based on Kalman filtering is designed. The system uses classical Kalman filtering algorithm to preprocess the tilt angle and stress data collected by the sensor, which effectively suppresses the noise interference. Based on the parallel computing advantage of FPGA, the functions of data acquisition, filtering and transmission are realized, and the monitoring data of bridge bearings are dynamically displayed in real time combined with the upper computer. The experimental results show that the signal-to-noise ratio of the filtered stress signal is 32.10 dB, the mean square error is 0.051 8, the correlation coefficient is 0.581 1, and the variance ratio is 0.701 8. The SNR, MSE, correlation coefficient and variance ratio of inclination signal after filtering are 33.98 dB, 0.041 8, 0.703 2 and 0.629 1, respectively, indicating that the filtering algorithm can effectively suppress noise interference. The developed system exhibits remarkable stability and reliability, achieving rapid data processing ability meanwhile satisfying the needs of real-time monitoring of bridge bearing inclination and stress. Additionally, this paper uses ASIC process to design the Kalman filter algorithm chip, and realizes the Kalman filter circuit based on 180 nm CMOS process. The clock frequency is 160 MHz, and the comprehensive area is 599 131 μm².