Nuclear cataract (NC) is the leading cause of blindness and vision impairment globally. Accurate NC classification is significant for clinical NC diagnosis. Anterior segment optical coherence tomography (AS-OCT) is a non-contact, high-resolution, objective imaging technique, which is widely used in diagnosing ophthalmic diseases. Clinical studies have shown that there is a significant correlation between the pixel density of the lens region on AS-OCT images and NC severity levels; however, automatic NC classification on AS-OCT images has not been seriously studied. Motivated by clinical research, this paper proposes a gated channel attention network (GCA-Net) to classify NC severity levels automatically. In the GCA-Net, we design a gated channel attention block by fusing the clinical priority knowledge, in which a gated layer is designed to filter out abundant features and a Softmax layer is used to build the weakly interacting for channels. We use a clinical AS-OCT image dataset to demonstrate the effectiveness of our GCA-Net. The results showed that the proposed GCA-Net achieves 94.3% in accuracy and outperformed strong baselines and state-of-the-art attention-based networks.