Abstract: The online monitoring system for railway slopes, based on the beidou global satellite navigation system, features all-weather, all-weather, high-precision, and high reliability. The effectiveness of system monitoring closely correlates with the data processing model. Taking the online monitoring system for slope deformation on the Shuohuang Railway as an example, this study focuses on three crucial aspects of data processing: data preprocessing, noise suppression, and deformation trend prediction. Initially, the 3σ criterion is employed for outlier detection in monitoring data, which is then corrected using the Kalman filter algorithm. Subsequently, the CLEAN algorithm, introduced to the field of deformation monitoring, is utilized to suppress noise, minimizing its impact on subsequent deformation trend predictions. Finally, an RBF neural network is applied for modeling and analyzing the noise-suppressed data to forecast current and future deformation trends of railway slopes. Engineering applications demonstrate that the proposed methods effectively detect and correct outliers, provide robust noise suppression, and yield precise deformation trend predictions, enhancing the practical application of monitoring systems.