The　accurate　identification　of　the　oil-paper　insulation　state　of　a　transformer　is　crucial　for　most　maintenance　strategies.　This　paper　presents　a　multi-feature　comprehensive　evaluation　model　based　on　combination　weighting　and　an　improved　technique　for　order　of　preference　by　similarity　to　ideal　solution　(TOPSIS)　method　to　perform　an　objective　and　scientific　evaluation　of　the　transformer　oil-paper　insulation　state.　Firstly,　multiple　aging　features　are　extracted　from　the　recovery　voltage　polarization　spectrum　and　the　extended　Debye　equivalent　circuit　owing　to　the　limitations　of　using　a　single　feature　for　evaluation.　A　standard　evaluation　index　system　is　then　established　by　using　the　collected　time-domain　dielectric　spectrum　data.　Secondly,　this　study　implements　the　per-unit　value　concept　to　integrate　the　dimension　of　the　index　matrix　and　calculates　the　objective　weight　by　using　the　random　forest　algorithm.　Furthermore,　it　combines　the　weighting　model　to　overcome　the　drawbacks　of　the　single　weighting　method　by　using　the　indicators　and　considering　the　subjective　experience　of　experts　and　the　random　forest　algorithm.　Lastly,　the　enhanced　TOPSIS　approach　is　used　to　determine　the　insulation　quality　of　an　oil-paper　transformer.　A　verification　example　demonstrates　that　the　evaluation　model　developed　in　this　study　can　efficiently　and　accurately　diagnose　the　insulation　status　of　transformers.　Essentially,　this　study　presents　a　novel　approach　for　the　assessment　of　transformer　oil-paper　insulation.　©　2023