Organic component microscopic identification quantitative method based on artificial intelligence

The invention provides an organic component microscopic recognition quantitative method based on artificial intelligence. The method comprises the following steps: S1, collecting rock sample matrix pictures in different illumination modes to a computer; s2, splicing the matrix pictures; s3, carrying out particulate matter edge tracking on the images in the different illumination modes, and obtaining particulate matter edge tracking maps in the different modes, wherein edge tracking adopts an edge fine detection network for identification; s4, superposing the particulate matter edge tracking maps in different modes, and reserving edge tracking path superposition; s5, performing classification extraction; s6, filling different colors according to classification; color filling adopts a deep learning open source model caffe based on a convolutional neural network as a learning model; and s7, counting the number of color pixels, and carryng out summing. Rapid microscopic identification of the relative content of t