The　aging　degree　of　an　oil-paper　insulation　system　is　closely　related　to　the　stable　operation　of　power　transformer　equipment.　There　is　low　accuracy　in　aging　diagnosis　when　it　relies　on　a　single　relevant　feature　quantity　obtained　by　frequency-domain　spectroscopy　(FDS)　and　does　not　consider　the　conflict　and　randomness　among　the　spectral　indicators.　Thus　a　new　diagnostic　method　for　determining　the　aging　degree　of　oil-paper　insulation　is　proposed.　This　method　integrates　spectral　characteristics　and　clustering　cloud-evidence　reasoning.　First,　the　multi-feature　indicators　for　aging　diagnosis　are　extracted　through　aging　samples’　spectral　characterization.　An　aging　feature　database　is　then　constructed　through　nonlinear　fitting.　Secondly,　a　basic　probability　assignment　method　for　the　clustering　cloud　model　is　proposed　based　on　K-means　clustering　to　accommodate　the　nonlinear　change　indicators.　Finally,　to　address　the　conflict　and　correlation　differences　of　multi-feature　quantities,　the　CRITIC-G1　comprehensive　assignment　method　is　used　to　calculate　the　evidence　correction　factor　for　each　indicator.　Subsequently,　the　basic　probability　is　reassigned,　and　evidence　fusion　inference　is　employed　to　determine　the　actual　aging　degree　of　the　system.　The　results　demonstrate　that　the　proposed　method　can　accurately　diagnose　the　aging　state　of　composite　oil-paper　insulation　samples　and　offers　theoretical　guidance　for　developing　power　maintenance　strategies.　©　2024　Power　System　Protection　and　Control　Press.　All　rights　reserved.