Keratoconus is a progressive corneal disease that mostly occurs in adolescence and can cause irregular astigmatism and vision loss. Late-stage blindness requires corneal transplantation. Therefore, early and accurate screening of keratoconus is necessary to prevent the progression of the disease and avoid deterioration. As a classic algorithm, neural network is an effective tool for keratoconus diagnosis. However, as the data of keratoconus cases grows day by day, in order to make full use of the new data, it is often necessary to retrain all samples, which will consume a lot of time. In order to solve the above problems, this article proposes an incremental learning algorithm integrating neural networks to achieve intelligent diagnosis of keratoconus. In addition, this article also introduces the ideas of undersampling and cost sensitivity to solve the problem that existing incremental learning algorithms cannot handle imbalanced data. Experimental results show that the recognition accuracy of the algorithm proposed in this article reaches 97%, and requires short training time and less storage space. Therefore, this algorithm can assist in the diagnosis of keratoconus more efficiently.