Ecotoxicity assessments, which rely on animal testing, face serious challenges, including high costs and ethical concerns. Computational toxicology presents a promising alternative
nevertheless, existing predictive models encounter difficulties such as limited datasets and pronounced overfitting. To address these issues, we propose a framework for predicting pesticide ecotoxicity using graph contrastive learning (PE-GCL). By pre-training on large-scale unlabeled compounds, the PE-GCL captured the intrinsic regulation of molecules. This knowledge is then transferred to specific downstream tasks, thereby enhancing the model generalization in scenarios with small sample sizes. Performance evaluation showed that the PE-GCL outperformed traditional supervised models across most prediction tasks, whereas independent external validation confirmed its superior predictive accuracy for unseen data. Furthermore, interpretability was incorporated to elucidate potential correlations between ecotoxicity and molecular substructures. The trained models were deployed on a publicly accessible web server (https://dpai.ccnu.edu.cn/PERA/) to facilitate the use of the proposed framework.