This　article　presents　a　hybrid　method　of　structural　modal　parameter　identification,　based　on　improved　empirical　mode　decomposition　(EMD)　and　autoregressive　and　moving　average　(ARMA).　Special　attention　is　given　to　some　implementation　issues,　such　as　the　modal　mixing,　false　modes,　the　judgment　of　the　real　intrinsic　mode　function　(IMF)　of　classical　EMD,　and　the　difficulty　of　fixing　the　order　of　ARMA.　To　resolve　the　existing　defects　of　EMD,　an　improved　EMD　(IEMD)　that　combines　frequency　band　filtering　and　cluster　analysis　is　proposed　in　this　paper,　where　frequency　band　filtering　divides　the　signal　into　several　narrowband　signals　before　the　EMD　process,　and　cluster　analysis　is　used　to　determine　the　real　IMFs.　Euclidean　distance　is　used　to　cluster　the　decomposition　results,　with　no　need　to　adjust　any　indexes　or　thresholds,　and　only　by　means　of　using　the　nearest　distance　to　efficiently　determine　the　real　IMF.　Moreover,　IEMD　is　used　as　a　pre-processing　tool　for　ARMA,　to　resolve　the　difficulty　of　fixing　its　order.　The　capabilities　of　the　proposed　method　were　compared　and　assessed　using　a　numerical　simulation　and　an　experimental　model.　The　numerical　simulation　and　experimental　results　showed　that　the　improved　method　could　resolve　the　modal　mixing　and　false　modal　problems　in　the　classical　EMD　process　and　could　automatically　identified　the　real　IMFs,　while　the　proposed　IEMD　was　combined　with　ARMA　to　successfully　identify　the　frequency　and　mode　shape　of　the　structure.　Additionally,　since　each　IMF　is　a　single　component　signal,　it　is　easy　to　determine　the　order　of　the　ARMA　model.