Abstract: The study of the spatiotemporal field function of the respirable dust concentration in fully mechanized excavation working face is the basis of dust prevention and early warning of pneumoconiosis.The key points of respirable dust concentration monitoring were analyzed, and the layout plan of measuring points of respirable dust concentration sensors was proposed.Taking the measured data of respirable dust concentration sensor at key monitoring points as the real-time source, the advantages and disadvantages of distance weighted inverse ratio method and Kriging method were compared and analyzed, and an extended spatial interpolation algorithm was established.The semi-variation theory was summarized, and the time dimension and space dimension of the change of respirable dust concentration were introduced into the semi-variation function to obtain a spatiotemporal field algorithm of respirable dust.The spatiotemporal field function model of respirable dust concentration in fully mechanized excavation working face was established.Finally, taking 2502 working face of Xinqiao Coal Mine in Henan Province as the object, the spatiotemporal field function was verified by the test based on the filter membrane weighing method.The results show that the concentration of respirable dust of the drivers from 5 m to 10 m in fully mechanized excavation working face and from 10 m to 15 m on the return air side is high and varies sharply, while the concentration of respirable dust begin to decrease steadily 15 m behind the return air side.The relative error of the calculated results of the spatiotemporal field function model of respirable dust concentration in fully mechanized excavation working face is ±14.8%, which is ±10.8% less than the Kriging method and ±22.4% less than the distance weighted inverse method.The results show that the spatiotemporal function model of respirable dust concentration in fully mechanized excavation working face incorporates the time dimension and space dimension into the weight coefficient, and the constructed spatiotemporal field function model improves the accuracy of interpolation calculation and the ability to remove specific value points.