Segmentation plays a crucial role in the computer-aided diagnosis of keratoconus. This paper proposes an accurate segmentation algorithm for the corneal deformation area in video images of corneal force deformation, based on a fully convolutional architecture integrated with an attention mechanism (AM). It includes three key technologies: skip connections (SC), residual convolution (RC), and a fully convolutional architecture integrated with global AM. Skip connections effectively enhance the model's ability to learn complex contour details, while RC allows for the construction of deeper feature models while retaining basic image features. The global AM improves the segmentation accuracy of the model by extracting refined feature maps from each convolutional and deconvolutional block. By enhancing and highlighting key areas, it has been demonstrated that more accurate segmentation of the corneal deformation area effectively improves the early diagnosis accuracy of keratoconus.