Vortex-induced　vibration　is　a　type　of　wind-induced　vibration　occurring　frequently　in　large-span　sea-crossing　bridges　under　relatively　low　wind　speeds,　posing　a　threat　to　the　structural　fatigue　performance　and　driving　comfort.　Identifying　the　instantaneous　occurrence　moments　of　vortex-induced　vibration　is　a　prerequisite　for　establishing　a　data-driven　prediction　model　for　vortex-induced　vibration,　and　it　is　of　great　significance　for　the　monitoring　and　early　warning　of　vortex-induced　vibration　performance　in　bridges.　To　automatically　detect　the　occurrence　moments　of　vortex-induced　vibration　and　establish　a　correlation　model　between　vortex-induced　vibration　amplitude　and　environmental　factors,　this　study　proposes　a　fuzzy　C-means　clustering-based　classification　method.　In　order　to　detect　the　occurrence　moments　of　vortex-induced　vibration　more　finely,　only　short-term　or　even　instantaneous　structural　vibration　indicators　were　selected　and　transformed　for　distribution　as　clustering　features.　The　entire　detection　process　could　be　carried　out　unsupervised,　reducing　the　manual　cost　of　obtaining　vortex-induced　vibration　information　from　massive　monitoring　data.　Finally,　actual　vortex-induced　vibration　test　data　from　a　certain　overseas　bridge　was　utilized　to　verify　the　feasibility　of　this　method.　Based　on　the　classification　results,　the　correlation　between　vortex-induced　vibration　amplitude　and　environmental　variables　was　determined,　providing　valuable　guidance　for　predicting　vortex-induced　vibration　amplitudes.