The invention discloses an automatic SAR image segmentation method based on graph division particle swarm optimization and mainly solves a problem of poor image segmentation effect in the prior art. The method comprises steps that 1, an original to-be-segmented image is inputted, and the gray information is read; 2, the to-be-segmented image is filtered to acquire a gradient image; 3, the gradient image is divided into non-overlapped regions; 4, the largest class quantity of the gradient image is solved and is taken as the largest image gray level; 5, the segmented regions are mapped to be undirected weighted graphs, and an energy function of the undirected weighted graphs is constructed; 6, iteration solution of the energy function is carried out to acquire a class center and the class quantity; and 7, whether iteration frequency is smaller than 20 is determined, if yes, particle update continues, if not, the optimal class quantity and the images after segmentation are outputted. The method is advantaged in that the operation speed is fast, the segmentation effect is good, and the method can be applied to medical images, satellite image positioning, face identification, fingerprint identification, traffic control systems and machine vision.