Cataract is a severe ophthalmic disease that significantly affects human visual function. To precisely assess the grading of visual acuity afflicted by cataracts, we propose a cataract visual acuity grading algorithm based on the efficient channel attention technique. This algorithm first employs the contrast limited adaptive histogram equalization (CLAHE) technique to preprocess fundus images, enhancing critical features such as blood vessels, optic disc, and macula. Subsequently, the efficient channel attention (ECA) mechanism is fused with a deep residual network to focus on fundus tissues and lesion areas relevant to visual acuity grading. To address the challenge of imbalanced dataset of fundus images, a focal loss (FL) function is introduced as the guiding objective for optimization, biasing the model towards patients with severe visual acuity. The algorithm is experimented with clinical data, achieving accuracies of 98.3%,90.5%, and 92.1% for normal vision, moderate vision cataract, and low vision cataract, respectively. The experimental results demonstrate that the proposed algorithm exhibits excellent performance in cataract visual acuity grading.