The calculation of a series of biomechanical parameters based on corneal deformation is the data foundation for training early keratoconus classification models,so the accuracy of keratoconus contour segmentation directly affects the accuracy of early keratoconus classification models.In this paper,we propose an unsupervised corneal video segmentation method based on residual networks.A set of anchor points are extracted by uniformly sampling the video frames in the same sequence,which reduces the computational complexity of the network model learning feature representation and improves computational efficiency.At the same time,a regularization branch is designed to transform the original video set for similarity to solve possible degenerate solutions.Compared with existing unsupervised video segmentation tasks,our experimental model uses a small amount of training data but achieves higher segmentation accuracy and computational efficiency.