The Qinling Mountains are an important geographic boundary in central and eastern China. The region has diverse and complex mountain ecosystems that are ideal to study the response of terrestrial ecosystems in the context of global climate change. Based on GIMMS NDVI data, meteorological data, and DEM and vegetation type data, we used the Comprehensive and CASA (Carnegie Ames Stanford Approach) models simulate NPP (Net Primary Productivity) and the response to climate change in the Qinling Mountains from 1982 to 2015. The research includes three main aspects: (1) MOD17A3 NPP data was used to compare the accuracy of the NPP values obtained by different methods. The NPP values calculated using the CASA model and GIMMS NDVI were most accurate without considering the vegetation type. (2) Changes in NPP were analyzed. The change trend of inter-annual and seasonal NPP was not significant temporally, but the inter-annual and spring NPP increased significantly, reaching 35.49% and 57.84% of the total study area, respectively, while the area of winter NPP significantly reduced by 22.87%. (3) The relationship between NPP and air temperature and precipitation was analyzed. The proportion of significant positively correlated inter-annual and spring NPP and precipitation values were higher, reaching 31.20% and 21.20%, respectively, while the proportion of significant positively correlated spring and autumn NPP values were only 10.80% and 10.20%, respectively. The complexity of the Qinling mountainous system enhances the heterogeneity of spatial and temporal variations in NPP and the response to climate change.