Regional zoning is an important way for humans to understand geographical space,but the existing research on regional zoning focuses on natural conditions, ignoring the interdependent characteristics of human and nature, and the driving mechanism of regional differentiation is not sufficiently explained. In this study, using algorithms such as self-organizing feature map, clustering quality index, structural similarity index measure (SSIM) and random forest, the human activity (HA)–natural endowment (NE) coupled zoning in the agro-pastoral ecotone in Gansu, China (AEGC), was conducted to explore the driving mechanism of regional differentiation. Results show the following: (1) The HA-NE coupled zoning has the best clustering effect when implementing the partitioning scheme with a classification number of 4. Accordingly, the AEGC can be divided into four regions with significant differences between HAs and NEs. (2) From the perspective of spatial distribution, HA–NE coupled zoning, climate zoning, geographic zoning, and vegetation zoning are similar, whereas HA zoning is different. The results of SSIM showed that HA–NE coupled zoning takes all types of factors into consideration (SSIM mean 0.708), and the zoning results are better than single type zonings. HAs have strong independence (SSIM mean 0.576), and geographic conditions are closely related to all other factors (SSIM mean 0.671). (3) Elevation is the most important driver of regional differentiation in the AEGC, with a contribution degree of 22.36%; other important drivers include land use intensity, precipitation, and normalized difference vegetation index, with contribution degree distributions of 17.38%, 16.34%, and 15.79%. Furthermore, the dominant factors of regional differentiation in each sub-region are different. This study emphasizes that regional characteristic factors and uneven spatial distribution factors are important drivers of regional differentiation, and land use intensity has become an important force influencing geographic space. Policy recommendations for zonal governance are made based on the inherent conditions of different regions. This study may provide a reference for scientific regional zoning and cognitive regional differentiation.