In　order　to　effectively　utilize　the　dielectric　response　characteristics　of　transformers　to　diagnose　the　insulation　state,　this　paper　proposes　a　two-level　hybrid　optimization　method　for　analyzing　time-domain　dielectric　response　characteristics.　The　optimization　algorithm　is　based　on　the　combined　statistical　indicators　(CSI)　and　random　forest　(RF)　theory.　The　initial　feature　space　set　is　formed　with　23　time-domain　characteristics.　In　the　first-level　stage,　statistical　indices　correlation,　distance,　and　information　indicators　are　integrated　to　assess　the　synthesis　score　of　the　characteristics,　while　highly　redundant　and　low-class　discrimination　characteristics　are　eliminated　from　the　initial　space　set.　In　the　second-level　stage,　the　Random　Forest　based　outside　bagging　data　theory　is　introduced　to　evaluate　the　least　important　characteristics,　and　the　characteristics　with　low　importance　indices　are　excluded　to　obtain　the　final　optimal　feature　space　set.　The　proposed　method　is　carried　out　on　82　sets　of　data　from　actual　dielectric　response　tests　on　oil-paper　insulation　transformers.　Finally,　the　final　optimal　feature　space　set,　along　with　several　other　data　sets,　is　tested　via　different　diagnosis　methods.　The　results　show　that　the　optimal　feature　space　set　obtained　via　the　proposed　method　outperforms　other　feature　space　sets　in　terms　of　better　adaptability　and　diagnosis　accuracy.