【Objective】Soil pH is crucial for ensuring soil fertility and maintaining the structure and function of ecosystems. Accurately identifying the key drivers of regional soil acidification provides an important basis for regulating soil acidification and improving soil quality in the study area. 【Method】This study takes Xianning District, Hubei Province as the research area. First, the optimal parameter geographic detector and grey slope correlation analysis models were used to identify the main drivers affecting farmland soil pH changes. On this basis, the structural equation model was employed to quantitatively analyze the driving mechanisms of arable soil. 【Result】The results show that over forty years, soil pH changed within a range of -2.39 to 2.19, with approximately 72.5% of the area showing a declining trend in pH. Comprehensive analysis of the two models indicates that organic matter, total potassium, and population density are core driving factors, while land use types and parent material are the dominant factors in the geographic detector, meaning they have strong spatial explanatory power but weaker process correlation. Nitrogen fertilizer application, annual sunshine hours, and distance to roads serve as balanced factors with moderate spatial explanatory power and process correlation. Curvature and near-surface SO2 concentration are factors with strong process correlation, which are considered in subsequent analysis in conjunction with theoretical knowledge. 【Conclusion】Structural equation modeling analysis further elucidates that soil organic matter and total potassium content have significant positive direct effects on changes in soil pH, whereas population density exerts a significant negative direct effect. Moreover, population density also forms a complex indirect driving network by influencing organic matter, total potassium, nitrogen fertilizer application, and near-surface SO2 concentration.