The increase in human activities is one of the important factors affecting the value of ecosystem services. However, understanding of the driving mechanisms of human activities is limited. We established a deep learning model to approximate the ecosystem service value (ESV) of Nanjing City using 23 socioeconomic factors. A multi-view analysis was then conducted on feasible impact mechanisms using model disassembly. The results indicated that factors such as the proportion of ecological waters in the land-use structure and secondary industry output value had their own independent effects on ESV. Other intrinsically related factors, for instance, industrial water consumption and industrial electricity consumption, were likely to be composited together to affect ESV.