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In the context of climate change, large-scale net primary productivity (NPP) estimation and its impact feature variables are drawing more and more attention. Traditional process-based and empirical models are limited by their model structure and input variable design. The available field measurement data are limited by their small coverage. We propose to train an NPP calculation model using random forest and quantify the influence of multiple meteorological features on NPP. The calculated NPP correlates well with the MODIS product (correlation coefficient higher than 0.8). The importance rankings of multiple features are related to the local economy and development strategy of research areas. In developed areas, vegetation indexes are the most important, while in developing areas, land classification type influences NPP the most. The experiments suggest random forest is promising for estimating NPP and useful in analysing the impact features in terms of global change.