Nitroaromatic　compounds　(NACs)　represent　a　significant　source　of　organic　pollutants　in　the　environment.　In　this　study,　a　well-rounded　dataset　containing　371　NACs　with　rat　oral　median　lethal　doses　(LD50s)　was　developed.　Based　on　the　dataset,　binary　and　multiple　classification　models　were　established.　Seven　machine　learning　algorithms　were　used　to　establish　the　prediction　models　in　combination　with　six　fingerprints.　In　the　binary　classification　models,　the　overall　predictive　accuracy　of　10-fold　cross-validation　for　training　set　in　the　top　ten　models　ranged　from　0.823　to　0.874.　In　the　multiple　classification　models,　the　combination　of　graph　fingerprint　and　random　forest　(Graph-RF)　yielded　the　best　predictive　effects　with　AUC　values　of　0.929　and　0.956　for　the　training　set　and　the　test　set,　respectively.　Model　prediction　performance　was　further　evaluated　using　the　true　external　set　comprising　1366　NACs,　including　96.6%　belonging　to　the　applicability　domain.　Further,　we　determined　the　structural　features　influencing　the　acute　oral　toxicity　based　on　information　gain　and　substructure　frequency　analysis.　Finally,　we　identified　highly　toxic　compounds　based　on　the　structural　alerts　and　successfully　transformed　a　representative　highly　toxic　compound　into　low-toxic　alternatives　via　structural　modification.　Overall,　the　models　constructed　facilitate　environmental　risk　assessment　and　the　design　of　green　and　safe　chemicals.