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Phenanthrene, a PAH with three fused benzene rings, is usually used as a model for the study on PAHs. During 4 days, 166 male mice were equally and randomly divided into two groups. One group was given vehicle-corn oil by oral gavage, the other was given phenanthrene at a dose of 450 milligrams per kilogram per day. In this study, in order to predict mice's phenanthrene poisoning by virtue of blood analysis indices, a new machine learning approach was put forward, which was based on an improved binary moth-flame optimizer combined with extreme learning machine. The results of the experiment have manifested that the blood analysis indices of the control and phenanthrene groups were significantly different (p<; 0.5). The most important correlated indices including serum alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), plateletcrit (PCT) and red blood cell distribution width-standard deviation (RDW-SD) were screened through feature selection. The classification results demonstrated that the proposed method can achieve 93.38% accuracy and 98.33% specificity. Promisingly, there is a new and accurate way to detect the status of phenanthrene poisoning expectably.