Abstract: In the context of climate change, the spatiotemporal pattern of vegetation in the Taihang Mountain area has undergone complex changes, which have far-reaching impacts on the coordinated development of the Beijing-Tianjin-Hebei region. Based on remote sensing images of vegetation NDVI from 2000 to 2022 in the hilly area (elevation <500 m), the middle mountain area (elevation 500−1 500 m), and the subalpine area (elevation >1 500 m) of the Taihang Mountain area, combined with methods such as linear trend analysis and regression analysis, we analyzed the spatial-temporal variation pattern of vegetation in the whole area and the three elevation partitions in the past 23 years and discussed the driving factors of vegetation change. The structural equation model (SEM) was used to analyze the direct and indirect effects of different factors on the spatiotemporal variation of vegetation NDVI. The results showed that, on the time scale, the NDVI in the Taihang Mountain area showed an overall upward trend from 2000 to 2022, with an average annual change rate of 0.005 8, while the NDVI in the hilly, middle mountain, and subalpine areas showed an increasing trend, with average annual change rates of 0.004 1, 0.006 6, and 0.005 7, respectively. The NDVI in the middle mountain area increased the most. On the spatial scale, the spatial distribution of NDVI in the Taihang Mountain area had an obvious heterogeneity over the past 23 years, with the overall characteristics of high NDVI in the south and low NDVI in the north, and the cross distribution of high and low NDVI in the middle section. The vegetation NDVI in 94% of the whole mountain area had increased, primarily concentrated in the middle mountain and subalpine areas. The NDVI in the Taihang Mountain area was significantly affected by climatic factors, among which the NDVI in hilly areas was more easily affected by temperature (correlation coefficient of −0.233), while in the middle mountainous and subalpine areas, the NDVI was more affected by precipitation (correlation coefficients of 0.369 and 0.511). Among the topographic factors, the NDVI increased with slope and initially increased and then decreased with elevation. In terms of human factors, population density and human footprint were all negatively correlated with vegetation NDVI variation, and the subalpine area was least affected by population density and human footprint (correlation coefficient of −0.036 and −0.325). SEM analysis showed that the most significant factors affecting NDVI in the entire mountainous, hilly, middle mountainous, and subalpine areas were slope (comprehensive effect value of 0.52), human footprint (−0.36), slope (0.58), and precipitation (0.52), respectively. Additionally, in the hilly area and middle mountain area, the vegetation NDVI was most directly influenced by the human footprint. In the subalpine area, vegetation NDVI was most directly influenced by precipitation. Elevation exerted the most significant indirect effects on vegetation NDVI across elevation partitions through factors such as slope and temperature. The research results are of great significance in clarifying the dynamic variation of vegetation in the Taihang Mountains area and the three elevation partitions under the background of climate change, and provide a theoretical basis for developing targeted ecological protection measures in the three elevation partitions.