Polycyclic　aromatic　hydrocarbons　(PAHs)　represent　a　common　group　of　environmental　pollutants　that　endanger　various　aquatic　organisms　via　various　pathways.　To　better　prioritize　the　ecotoxicological　hazard　of　PAHs　to　aquatic　environment,　we　used　2D　descriptors-based　quantitative　structure-toxicity　relationship　(QSTR)　to　assess　the　toxicity　of　PAHs　toward　six　aquatic　model　organisms　spanning　three　trophic　levels.　According　to　strict　OECD　guideline,　six　easily　interpretable,　transferable　and　reproducible　2D-QSTR　models　were　constructed　with　high　robustness　and　reliability.　A　mechanistic　interpretation　unveiled　the　key　structural　factors　primarily　responsible　for　controlling　the　aquatic　ecotoxicity　of　PAHs.　Furthermore,　quantitative　read-across　and　different　machine　learning　approaches　were　employed　to　validate　and　optimize　the　modelling　approach.　Importantly,　the　optimum　QSTR　models　were　further　applied　for　predicting　the　ecotoxicity　of　hundreds　of　untested/unknown　PAHs　gathered　from　Pesticide　Properties　Database　(PPDB).　Especially,　we　provided　a　priority　list　in　terms　of　the　toxicity　of　unknown　PAHs　to　six　aquatic　species,　along　with　the　corresponding　mechanistic　interpretation.　In　summary,　the　models　can　serve　as　valuable　tools　for　aquatic　risk　assessment　and　prioritization　of　untested　or　completely　new　PAHs　chemicals,　providing　essential　guidance　for　formulating　regulatory　policies.