Large foundation models (LFMs) have recently gained attention in the computer vision field. Recently, several LFMs focused on ophthalmic imaging were pre-trained and achieved promising performance on different downstream tasks. However, few have explored how to leverage the pre-trained ophthalmic LFM to improve the accuracy of fundus disease identification. Currently, commonly used fine-tuning and transfer learning methods for training pre-trained LFM to recognize fundus diseases are susceptible to the influence of dataset feature distribution, resulting in limited performance when identifying fundus diseases with complex or ambiguous pathological features. Therefore, exploring how to effectively utilize the pre-trained ophthalmic LFM to improve the performance of identifying fundus diseases with different feature distributions is crucial and remains an open challenge. Focusing on these challenges, we propose a novel contrastive enhanced low-rank learnable adaptation prompt (CE-LORA) to enhance the performance of foundation models in fundus disease identification tasks. Our proposed method introduces low-rank representation reparameterization combined data augmentation contrastive learning category to achieve parameter-efficient transfer learning, guiding the model to focus on category-related features while enhancing the model’s capacity to represent discriminative features contained in fundus images. We analyzed the performance of different methods on a dataset collected from multiple ophthalmic clinics with varying feature distributions. Our proposed CE-LORA increased the average F1 score of the LFM model by 36.74% and 14.94% compared to commonly used approaches of fine-tuning and transfer learning, respectively, reaching 94.29%.