Principal component analysis (PCA) has been widely used in the integrated soil fertility quality index (SFQI) (SFQI_PCA) assessment. However, the traditional PCA, only established in variable space, does not consider the spatially varying relationships among soil fertility indicators in large-scale areas, and thus cannot appropriately determine the spatially varying relative importance (i.e., weight calculated based on communality) of soil indicators in the SFQI assessment. Moreover, uncertainty inevitably exists in the spatial distribution pattern of SFQI due to the limited sample points, which is critical to the precision management of soil fertility. To address these limitations, this study first proposed a novel assessment method for SFQI based on geographically weighted principal component analysis (GWPCA) (SFQI_GWPCA). Secondly, SFQI_GWPCA was assessed in Shayang County, China, and then compared with the traditional SFQI_PCA. Finally, the spatial uncertainty of SFQI_GWPCA was assessed based on the 1000 realizations generated by sequential Gaussian simulation (SGS). Results showed that (i) spatially varying relationships among soil indicators were revealed by Monte Carlo test and GWPCA outputs (i.e., the winning variable and the local percentage of total variation), while traditionally-used PCA was conducted in the assumption of spatially stationary relationships among soil indicators; (ii) in SFQI assessment, the spatially varying indicator weights were determined by GWPCA, but could not be determined by PCA; (iii) the areas with higher threshold-exceeding probability (≥ 0.95) mainly located in the northwest of this county, and the areas with lower threshold-exceeding probability (≤ 0.05) mainly located in the east of this county. It is concluded that GWPCA adequately considers the spatially varying relationships among soil indicators, and SFQI_GWPCA is an effective tool in the SFQI assessment in large-scale areas.