Long-span　bridges　are　susceptible　to　damage,　aging,　and　deformation　in　harsh　environments　for　a　long　time.　Therefore,　structural　health　monitoring　(SHM)　systems　need　to　be　used　for　reasonable　monitoring　and　maintenance.　Among　various　indicators,　bridge　displacement　is　a　crucial　parameter　reflecting　the　bridge＇s　health　condition.　Due　to　the　simultaneous　bearing　of　multiple　environmental　loads　on　suspension　bridges,　determining　the　impact　of　different　loads　on　displacement　is　beneficial　for　the　better　understanding　of　the　health　conditions　of　the　bridges.　Considering　the　fact　that　extreme　gradient　boosting　(XGBoost)　has　higher　prediction　performance　and　robustness,　the　authors　of　this　paper　have　developed　a　data-driven　approach　based　on　the　XGBoost　model　to　quantify　the　impact　between　different　environmental　loads　and　the　displacement　of　a　suspension　bridge.　Simultaneously,　this　study　combined　wavelet　threshold　(WT)　denoising　and　the　variational　mode　decomposition　(VMD)　method　to　conduct　a　modal　decomposition　of　three-dimensional　(3D)　displacement,　further　investigating　the　interrelationships　between　different　loads　and　bridge　displacements.　This　model　links　wind　speed,　temperature,　air　pressure,　and　humidity　with　the　3D　displacement　response　of　the　span　using　the　bridge　monitoring　data　provided　by　the　GNSS　and　Earth　Observation　for　Structural　Health　Monitoring　(GeoSHM)　system　of　the　Forth　Road　Bridge　(FRB)　in　the　United　Kingdom　(UK),　thus　eliminating　the　temperature　time-lag　effect　on　displacement　data.　The　effects　of　the　different　loads　on　the　displacement　are　quantified　individually　with　partial　dependence　plots　(PDPs).　Employing　testing,　it　was　found　that　the　XGBoost　model　has　a　high　predictive　effect　on　the　target　variable　of　displacement.　The　analysis　of　quantification　and　correlation　reveals　that　lateral　displacement　is　primarily　affected　by　same-direction　wind,　showing　a　clear　positive　correlation,　and　vertical　displacement　is　mainly　influenced　by　temperature　and　exhibits　a　negative　correlation.　Longitudinal　displacement　is　jointly　influenced　by　various　environmental　loads,　showing　a　positive　correlation　with　atmospheric　pressure,　temperature,　and　vertical　wind　and　a　negative　correlation　with　longitudinal　wind,　lateral　wind,　and　humidity.　The　results　can　guide　bridge　structural　health　monitoring　in　extreme　weather　to　avoid　accidents.