Abstract: Under the background of climate change, spatial-temporal pattern of vegetation in Taihang Mountain area has undergone complex changes, which has a far-reaching impact on the coordinated development of 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 was used to analyze the direct and indirect effects of different factors on the spatial-temporal variation of vegetation NDVI. The results showed that: On the time scale, the NDVI in Taihang Mountain area showed an overall upward trend from 2000 to 2022, with an average annual change rate of 0.005 8; while on the elevation gradient, the NDVI in hilly area, middle mountain area and subalpine area all showed an increasing trend, with average annual change rates of 0.004 1, 0.006 6, and 0.005 7 respectively, and NDVI in middle mountain area increased the most. On the spatial scale, the spatial distribution of NDVI in the Taihang Mountain area had an obvious heterogeneity in 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, and the vegetation NDVI in 94% of the whole mountain area had increased, primarily concentrated in the middle mountain area and subalpine area. The NDVI in Taihang Mountain area was significantly affected by climate factors, among which the NDVI in hilly area was more easily affected by temperature (correlation coefficient of −0.233), while in the middle mountain area and the subalpine area, NDVI was more affected by precipitation (correlation coefficient of 0.369 and 0.511). Among the topographic factors, NDVI increased with slope and initially increased then decreased with elevation. In terms of human factors, population density, human footprint, and vegetation NDVI variation were all negatively correlated, and the subalpine area was least affected by population density and human footprint (correlation coefficient of −0.036 and −0.325). SEM model analysis showed that the most significant factors affecting NDVI in the whole mountain area, hilly area, middle mountain area and subalpine area were slope (comprehensive effect value of 0.52), the human footprint (−0.36), slope (0.58) and precipitation (0.52) respectively. Additionally, across all elevation partitions, except for the subalpine area, vegetation NDVI is most directly influenced by human footprint. In the subalpine area, vegetation NDVI is most directly influenced by precipitation. Elevation exerts 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 to clarify the dynamic variation of vegetation in Taihang Mountain area and the three elevation partitions under the background of climate change, and provide theoretical basis for developing targeted ecological protection measures in the three elevation partitions.