It is of great significance to have a detailed and accurate inventory of anthropogenic CO₂ emissions and to strengthen the effective control of CO₂ emissions in order to achieve the carbon peak and neutrality targets. Therefore, this study evaluated the reliability of EDGAR and ODIAC global anthropogenic CO₂ emission inventories in China and developed a high-accuracy and high-resolution estimation model for anthropogenic CO₂ emission based on multi-source remote sensing data and the Random Forest algorithm. The anthropogenic CO₂ emissions from 2005 to 2020 at the resolution of 1 km×1 km were estimated and the clustering patterns of anthropogenic CO₂ emissions were explored using the spatial autocorrelation. The spatial and temporal variations of anthropogenic CO₂ emissions in China and key areas of clustering patterns were then analysed. Results show that the estimation model of anthropogenic CO₂ emissions in China can effectively integrate the advantages of global inventory and remote sensing data, providing valid support for the spatial and temporal estimation of anthropogenic CO₂ emissions at a fine scale; from 2005 to 2013, China's anthropogenic CO₂ emissions increased dramatically from 5.53 billion tonnes to 10.73 billion tonnes; the growth rate of CO₂ emissions declined in 2013-2020, with changes tending to flatten out; China's overall anthropogenic carbon emissions demonstrate regional characteristics of "high in the east and low in the west, high in the coastal and low in the inland", clustering in the focus areas of Yangtze River Delta, Beijing-Tianjin-Hebei region and western region; the characteristics of anthropogenic carbon emissions in different regions are closely related to their industrialization process, level of economic development, scale of counties and so on.