Abstract: In the report, in order to improve the accuracy of vegetation classification under rugged terrain conditions, the Integrated Topographic Correction (ITC) model was used to correct the topography of Landsat remote sensing images of Fuzhou City in 2014 and 2023, and the Random Forest (RF) algorithm and the Recursive Feature Elimination (RFE) algorithm were used for feature selection to construct an optimal feature subset that eliminates the impact of terrain. Ultimately, a Random Forest classifier was used for vegetation classification. The Rate of Change was used to elucidate the degree of dynamic change of each vegetation type in Fuzhou City from 2014 to 2023. The driving factors behind vegetation changes were explored. The results indicated that ITC model effectively restored the spectral data of self and cast shadows to the level of sunny areas. After correction, the overall accuracy and Kappa coefficient of vegetation classification were significantly improved. From 2014 to 2023, the total vegetation area in Fuzhou City showed a decreasing trend, with a land-use dynamic change rate at −0.71%. Factor detection revealed that the driving factors of vegetation spatial changes at different stages are significant different, however, temperature, soil type, and nighttime light brightness are the key influencing factors. Interaction detection showed that the factors exhibited dual-factor enhancement or nonlinear enhancement interactions across all years, which suggested that the interactions among the factors further accelerated the spatial changes of vegetation.