Bridge　structures　are　susceptible　to　environmental　and　operational　variations　(EOVs).　Improperly　handling　these　influences　may　result　in　incorrect　assessments　of　the　bridge＇s　health　condition.　Blind　source　separation　(BSS)　techniques　show　promising　potential　in　suppressing　the　effects　of　EOVs.　However,　major　challenges　such　as　high　data　variability,　difficulty　in　parameter　selection,　lack　of　reliable　decision　thresholds,　and　practical　engineering　validation　have　seriously　hindered　the　application　of　such　techniques　in　bridge　health　monitoring.　Consequently,　this　paper　proposes　a　new　method　for　bridge　damage　detection　that　combines　complexity　pursuit　(CP)　and　extreme　value　theory　(EVT).　This　method　first　uses　the　exponentially　weighted　moving　average　(EWMA)　technique　to　preprocess　the　measured　modal　frequencies.　The　CP　algorithm　and　information　entropy　are　then　used　to　extract　structural　damage　sources　from　the　preprocessed　data　automatically.　Based　on　the　extracted　structural　damage　sources,　the　damage　index　(DI)　is　defined　using　k-means　clustering　and　Euclidean　distance.　Following　that,　the　generalized　extreme　value　(GEV)　distribution　is　used　to　fit　the　DI　data　under　the　normal　condition　of　the　bridge,　and　the　damage　detection　threshold　is　given　according　to　the　fitted　distribution.　Benchmark　data　of　the　KW51　railway　bridge　are　considered　to　verify　the　effectiveness　of　the　proposed　method　along　with　several　comparative　studies.　The　results　show　that　even　under　strong　EOV　influences,　the　proposed　method　still　maintains　good　damage　detection　accuracy　and　robustness,　and　its　effectiveness　is　superior　to　some　well-known　damage　detection　methods.