Multivariate statistical analysis methods,principal component analysis and partial least square discrimination analysis,were applied in this study for the data mining of cigarette smoke exposure metabolomics on plasma,urine and lung samples,in order to characterize the holistic influences of cigarette smoke exposure,and screen potential biomarkers.The screened biomarkers obtained from the metabolic profiling analysis on plasma,urine and lung were integrated and reduced by neurofuzzy logic.The predictability of the established model with this focused biomarkers were evaluated by artificial neural networks.Key biomarkers were closely related to different smoke exposure time(7,14,30 days),and different kinds of cigarette smoke exposure on the endogenous metabolites in rats were found in this study,and the damage mechanism of cigarette smoke exposure on rat′s organism was discussed.