Bayesian　model　updating　based　on　variational　inference　(VI-BMU)　methods　have　attracted　widespread　attention　due　to　their　excellent　computational　tractability.　Traditional　VI-BMU　methods　often　employ　the　mean-field　assumption,　which　simplifies　computation　by　treating　model　parameters　as　independent.　Recent　advances　in　variational　inference　have　introduced　more　flexible　variational　distributions,　enabling　accurate　modeling　of　parameter　dependencies.　To　further　address　the　limitations　of　traditional　VI-BMU　methods,　this　paper　introduces　a　novel　Bayesian　model　updating　framework　based　on　variational　inference　and　Gaussian　copula　model　(VGC-BMU).　This　framework　incorporates　Gaussian　copula　model　to　simulate　the　dependency　relationships　between　model　parameters,　significantly　improving　the　accuracy　of　posterior　distribution　estimation.　The　theoretical　relationship　between　the　VGC-BMU　and　traditional　VI-BMU　is　derived,　and　the　necessity　and　advantages　of　parameter　dependency　modeling　are　elucidated.　Moreover,　a　simplified　computational　approach　is　developed　by　introducing　Jacobian　matrix　transformations　and　parameter　expansion　techniques　to　address　the　high　computational　complexity　of　the　VGC-BMU.　The　method＇s　capability　to　identify　parameter　dependencies　is　first　demonstrated　through　two　simple　numerical　models.　Subsequently,　two　engineering　case　studies-a　four-story　shear　frame　model　and　a　steel　pedestrian　bridge　model-are　selected　to　evaluate　the　performance　of　VGC-BMU　in　parameter　identification　and　dependency　modeling.　The　results　demonstrate　that　VGC-BMU　significantly　improves　parameter　identification　accuracy　compared　to　traditional　VI-BMU　methods.　Furthermore,　VGC-BMU　exhibits　superior　accuracy　and　robustness　in　response　prediction　by　incorporating　parameter　dependency　modeling,　making　it　a　more　effective　and　reliable　approach　for　engineering　applications.