The onset of subclinical keratoconus (subkc) is hidden, and existing medical equipment has limitations in diagnosis. Therefore, it is necessary to propose a detection method for diagnosing subclinical keratoconus. Studies have found that the mechanical properties of keratoconus (kc) change earlier than morphology, so screening subclinical keratoconus from the perspective of corneal biomechanics is more in line with clinical practice. This article utilizes corneal biomechanical features and uses point cloud data as network input data. Self organizing network (SO- Net) and self attention mechanism (SA) are combined to construct SOANet, which classifies keratoconus, subclinical keratoconus, and normal corneas. Firstly, a corneal visualization Scheimpflug technology (Corvis ST) was used to capture a corneal deformation video, which was processed to obtain a point cloud dataset. The point cloud data was then enhanced to achieve a balanced distribution of the three types of corneal data. Then the training set and test set were divided in a 3∶1 ratio, and the cornea was classified into two categories and three categories, respectively. The accuracy of the final model on the two categories and three categories test sets reached 98.3% and 91.26%, respectively, effectively identifying subclinical keratoconus and keratoconus. The experimental results indicate that constructing a subclinical keratoconus assisted diagnostic model using 3D point cloud data is feasible, and SOANet can effectively recognize subclinical keratoconus, and its classification performance is better than traditional models.