“Significant breakthroughs have been made in the field of remote sensing monitoring of industrial heat sources. In response to issues such as unclear heat source characteristics and inaccurate type determination, the research team proposed an industrial heat source artificial neural network remote sensing classification and accurate recognition method coupled with temperature characteristics. This method uses DBSCAN clustering algorithm and land use type to identify industrial heat sources, establishes temperature feature templates using frequency statistical methods, and constructs an artificial neural network model for heat source type discrimination. Research has found significant differences in temperature frequency and distribution patterns among different industrial heat sources, with main peak temperatures of 795 K, 830 K, 760 K, and 1725 K, respectively. In addition, the model performs well in industrial heat source classification and recognition, with training set and validation classification recognition accuracies of up to 99% and 88.17%, respectively. The study also found that the spatial and temporal distribution of industrial heat sources in China exhibits dual characteristics of "regional concentration" and "fluctuating decline", mainly concentrated in the northern region, accounting for as much as 85.4% of the total. This research achievement provides technical support for satellite based remote sensing monitoring of atmospheric industrial pollution sources, and is expected to promote new progress in China's air pollution prevention and control work.”