To　better　understand　the　mechanism　of　in　vivo　toxicity　of　N-nitroso　compounds　(NNCs),　the　toxicity　data　of　80　NNCs　related　to　their　rat　acute　oral　toxicity　data　(50%　lethal　dose　concentration,　LD50)　were　used　to　establish　quantitative　structure-activity　relationship　(QSAR)　and　classification　models.　Quantum　chemistry　methods　calculated　descriptors　and　Dragon　descriptors　were　combined　to　describe　the　molecular　information　of　all　compounds.　Genetic　algorithm　(GA)　and　multiple　linear　regression　(MLR)　analyses　were　combined　to　develop　QSAR　models.　Fingerprints　and　machine　learning　methods　were　used　to　establish　classification　models.　The　quality　and　predictive　performance　of　all　established　models　were　evaluated　by　internal　and　external　validation　techniques.　The　best　GA-MLR-based　QSAR　model　containing　eight　molecular　descriptors　was　obtained　with　Q(loo)(2)　=　0.7533,　R-2　=　0.8071,　Q(ext)(2)　=　0.7041　and　R-ext(2)　=　0.7195.　The　results　derived　from　QSAR　studies　showed　that　the　acute　oral　toxicity　of　NNCs　mainly　depends　on　three　factors,　namely,　the　polarizability,　the　ionization　potential　(IP)　and　the　presence/absence　and　frequency　of　C-O　bond.　For　classification　studies,　the　best　model　was　obtained　using　the　MACCS　keys　fingerprint　combined　with　artificial　neural　network　(ANN)　algorithm.　The　classification　models　suggested　that　several　representative　substructures,　including　nitrile,　hetero　N　nonbasic,　alkylchloride　and　amine-containing　fragments　are　main　contributors　for　the　high　toxicity　of　NNCs.　Overall,　the　developed　QSAR　and　classification　models　of　the　rat　acute　oral　toxicity　of　NNCs　showed　satisfying　predictive　abilities.　The　results　provide　an　insight　into　the　understanding　of　the　toxicity　mechanism　of　NNCs　in　vivo,　which　might　be　used　for　a　preliminary　assessment　of　NNCs　toxicity　to　mammals.