State-of-health　(SOH)　of　lithium-ion　batteries　is　an　important　indicator　for　measuring　performance　and　remaining　life.　We　propose　an　innovative　prediction　model　that　integrates　variational　mode　decomposition　(VMD),　Dung　Beetle　optimizer　(DBO),　and　support　vector　regression　(SVR)　algorithms.　We　extracted　relevant　features　from　the　discharge　characteristic　curve　and　incremental　capacity　curve.　We　used　Pearson　and　Spearman　correlation　coefficient　methods　for　correlation　analysis　on　the　extracted　health　factors　(HFs),　selecting　those　that　significantly　impact　SOH　as　input　features.　A　DBO-SVR　model　was　constructed　to　establish　a　nonlinear　correlation　between　HFs　and　SOH,　and　the　DBO　algorithm　was　used　to　globally　search　and　optimize　the　hyperparameters　of　the　SVR　model　to　improve　its　prediction　accuracy.　To　reduce　the　impact　of　noise　in　battery　signals　on　model　performance,　VMD　technology　was　introduced　to　decompose　battery　signals　into　multiple　intrinsic　mode　components,　to　extract　useful　features　and　remove　noise　to　further　improve　prediction　accuracy.　The　proposed　method　was　validated　using　the　NASA　battery　dataset　and　compared　with　other　algorithm　models.　Results　showed　that　the　prediction　model　was　significantly　better　than　other　models,　with　a　maximum　RMSE　value　of　0.84%,　a　maximum　MAE　value　of　0.71%,　and　a　stable　prediction　error　value　within　1%.