China Agricultural University and the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, have developed a national mapping method to address the lack of high-resolution, open-source data on the distribution of ground-based photovoltaic power stations in China. This method uses partition modeling and random forest classification technology, combined with active learning strategies for optimization, and uses multispectral images from the European Space Agency's Sentinel-2 satellite.The 2020 national 10-meter resolution ground photovoltaic power station classification product was successfully released.This achievement not only accurately reveals the spatial distribution characteristics of my country's photovoltaic power stations, but also provides valuable data resources for energy planning, land use, remote sensing monitoring and environmental research with a classification accuracy of 89%. This fills the gap in domestic data in this field and is of great significance to related research.