Keratoconus causes the central cornea to bulge forward during the disease process,giving the cornea a conical shape,and leading to highly irregular myopia and astigmatism,causing damage of vision with different degrees.The disease generally occurs in the adolescent period,in order to timely treat and to avoid serious lesions, it is of great significance to screen and distinguish keratoconus.In addition,clinical diagnosis of keratoconus is usually detected by corneal topography,which can obtain morphological changes of the cornea,but there is a certain misdiagnosis rate.At present,it has been found that the change of mechanical properties of cornea is prior to morphology.Therefore,from the perspective of corneal biomechanics,this paper proposed a model to distinguish keratoconus based on multi-layer perceptron (MLP) neural network.Firstly,corneal visualization scheimpflug technology (Corvis-ST) was used to measure the biomechanical video of cornea,and corneal biomechanical parameters were obtained as a data set,including normal cornea and keratoconus.Then,MLP neural network model was constructed for corneal biomechanical parameter data sets,in which 70% data sets were used as training sets and 30% as test sets.The results of training and testing on the datasets showed that the accuracy of keratoconus differentiation was 97.6%.