(1) Background: Marine eutrophication represents a formidable challenge to sustainable global aquaculture, posing a severe threat to marine ecosystems and impeding the achievement of UN Sustainable Development Goal 14. Current methodologies for identifying eutrophication events and tracing their drivers from vast, heterogeneous text data rely on manual analysis and thus have significant limitations. (2) Methods: To address this issue, we developed a novel automated attribution analysis framework. We first pre-trained a domain-specific model (Aquaculture-BERT) on a 210-million-word corpus, which is the foundation for constructing a comprehensive Aquaculture Eutrophication Knowledge Graph (AEKG) with 3.2 million entities and 8.5 million relations. (3) Results: Aquaculture-BERT achieved an F1-score of 92.1% in key information extraction, significantly outperforming generic models. The framework successfully analyzed complex cases, such as Xiamen harmful algal bloom, generating association reports congruent with established scientific conclusions and elucidating latent pollution pathways (e.g., pond aquaculture–nitrogen input–Phaeocystis bloom). (4) Conclusions: This study delivers an AI-driven framework that enables the intelligent and efficient analysis of aquaculture-induced eutrophication, propelling a paradigm shift toward the deep integration of data-driven discovery with hypothesis-driven inquiry. The framework provides a robust tool for quantifying the environmental impacts of aquaculture and identifying pollution sources, contributing to sustainable management and achieving SDG 14 targets.
Hao et al. (Tue,) studied this question.