Accurate　diagnosis　of　the　composite　oil-paper　insulation　state　of　power　transformers　is　of　great　guiding　significance　for　the　safe　and　stable　operation　of　power　systems　and　the　operation　and　maintenance　of　equipment　itself.　In　this　paper,　an　evaluation　method　based　on　grey　relational　analysis　(GRA)　and　a　clustering　cloud　model　is　proposed　to　solve　the　problem　of　inaccurate　evaluation　caused　by　few　characteristic　quantities　of　the　dielectric　response　of　oil-paper　insulation　and　failure　to　consider　the　randomness　of　the　system.　First,　based　on　the　recovery　voltage　method　and　the　extended　Debye　model,　five　relevant　features　are　extracted　to　establish　the　oil-paper　insulation　state　evaluation　system.　Second,　in　view　of　the　sensitivity　differences　of　multiple　feature　quantities　in　the　reactive　insulation　state,　a　combination　weighting　method　combining　GRA　and　an　improved　analytic　hierarchy　process　is　used　to　avoid　data　information　loss　and　make　the　weight　allocation　more　reasonable.　Finally,　it　uses　the　atomization　characteristics　of　the　cloud　model　to　reflect　the　randomness　of　the　data,　and　comprehensively　considers　the　randomness　and　fuzziness　of　the　classification　boundary　of　the　evaluation　index　grade.　After　that　a　clustering　cloud　model　membership　selector　is　constructed.　The　validation　of　measured　data　from　multiple　transformers　with　different　furfural　content　shows　that　the　evaluation　method　can　not　only　accurately　reflect　the　actual　insulation　status　of　the　transformer,　but　also　reflect　its　deterioration　trend,　providing　a　reference　basis　for　the　formulation　of　maintenance　strategies.　©　2023　Power　System　Protection　and　Control　Press.　All　rights　reserved.