The　nature　of　damage　identification　is　close　to　a　pattern　recognition　process　that　classifies　different　damage　patterns.　The　na　&　iuml;ve　Bayes　classifier　(NBC)　can　effectively　handle　multiple-classification　problems　by　choosing　patterns　with　high　probabilities.　Therefore,　by　absorbing　the　autoregressive　model　with　exogenous　inputs　(the　ARX　model),　an　ARX-Na　&　iuml;ve　Bayes　damage　identification　strategy　has　been　proposed　in　which　the　autoregressive　coefficients　of　the　ARX　model　are　taken　as　the　sensitive　damage　feature.　The　classification　training　and　test　sample　datasets　are　then　built　on　these　coefficients　corresponding　to　various　damage　scenarios.　The　model　order　of　an　AR　model　is　first　determined　for　the　subsequent　order　selection　of　the　ARX　model,　whose　autoregressive　coefficients　are　further　used　to　construct　the　NBC.　This　procedure　can　enhance　the　pattern　recognition　robustness　to　uncertainties　such　as　measurement　noises.　Different　damage　patterns　are　determined　by　calculating　the　sum　of　logarithmic　likelihoods　of　testing　samples.　The　effectiveness　of　the　proposed　method　has　been　verified　against　a　bridge　benchmark　model　having　different　damage　scenarios　under　the　noise　pollution.　In　addition,　an　experimental　five-story　shear　frame　structure　was　adopted　for　validation,　showing　that　compared　with　the　SVM　algorithm　suitable　for　handling　binary　classification　problems,　the　proposed　method　excels　in　multi-classification　of　damage　patterns.