In order to accurately determine the degree of abrasion of abrasive belts in grinding screw rotors, the degree of abrasion of abrasive belts is identified according to the law of change of the color characteristics and texture characteristics of the surface image in the abrasion process of abrasive belts. The texture features and color features of the surface image of the abrasive belt after the grinding process are extracted, and the abrasive belt wear degree is classified according to the surface roughness of the screw rotor in different grinding time periods. The classification performance of the support vector machine is greatly affected by its own kernel function and penalty function, so it is proposed to optimize the kernel parameter and penalty parameter of the support vector machine by using the aquila optimizer optimization algorithm, and to establish the wear degree model of AO-SVM sand belt image recognition. The experiment is completed by utilizing a self-developed special belt grinding device for screw rotors. The grinding parameters are set as follows: the linear speed of the grinding belt is 10 m/s, the axial feed speed of the workpiece is 50 mm/min, the cylinder pressure of the tensioning wheel is 0.35 MPa, the cylinder pressure of the active wheel is 0.5 MPa, and the grinding time is 25 min. The recognition accuracy of AO-SVM for the abrasive belt wear degree model reaches 92.5%, which is improved by 5.0% and 3.6% compared to the random forest algorithm (RFC) and the XGboost classification algorithm, respectively, and the convergence speed is fast. The degree of abrasive belt wear can be identified by the AO-SVM model through the color feature change and texture feature change of the surface image of the abrasive belt, which can effectively avoid excessive abrasive belt wear and damage to the workpiece, and provide theoretical guidance for judging the degree of abrasive belt wear and the time to change the abrasive belt when the abrasive belt is used to grind the screw rotor.