Abstract: Based on vegetation, land use, and meteorological data, hotspot analysis, trend analysis, correlation analysis, and geographical detector was used to investigate the spatiotemporal distribution characteristics and influencing factors of Vegetation Carbon Use Efficiency (CUE) in the vegetation of the Taihang Mountains during 2001-2018. The following results were obtained: 1) Gross Primary Productivity (GPP) and Net Primary Productivity (NPP) of the vegetation in the Taihang Mountains exhibited an increasing trend during 2001-2018, while the growth rate of vegetation NPP was lower than that of vegetation GPP, leading to a decreasing trend in the vegetation CUE. The average CUE was 0.55, demonstrating a distribution pattern of “higher in the center, lower around”; moreover, 52.40% of the area exhibited a downward trend in CUE. 2) Vegetation CUE was significantly positively correlated with precipitation, soil moisture, and WUE; the influence order was precipitation＞ WUE＞ soil moisture. CUE was highest in forest, followed by grassland, then cropland. Along elevation, GPP, NPP, and CUE followed a parabolic (increase–decrease) pattern, with peak CUE (0.58) at 1500~2000 m. 3) Geographical detector analysis revealed that the explanatory power of individual influencing factors on CUE was as follows: precipitation＞ vegetation WUE＞ soil moisture＞ temperature＞ land use types＞ elevation, with the interaction between precipitation and temperature exerting the strongest influence on the spatial differentiation of CUE. This study, through long-term sequential analyses of CUE spatiotemporal dynamics, combined with multivariate correlation analysis and geographical detector–based quantification of influencing factor importance, reveals the intrinsic links between spatiotemporal patterns and the driving mechanisms of vegetation CUE, thereby providing a theoretical basis for sustainable vegetation development management in the Taihang Mountain area.