Near　infrared　spectroscopy　(NIRS),　as　a　fast　and　nondestructively　analytic　technique,　is　a　promising　tool　in　on-site　aging　condition　assessment　of　oil-paper　insulated　transformers.　However,　some　key　steps　such　as　the　assessment　modeling　and　filed　applications　are　still　in　the　lab-exploring　stages.　This　paper　presented　the　quantitative　evaluation　models　based　on　NIRS　to　predict　the　degree　of　polymerization　(DP)　of　insulating　paper,　which　is　directly　indicating　the　ageing　condition.　The　NIRS　coupled　with　DP　measurements　were　performed　on　Kraft　papers　with　varying　ageing　conditions　by　thermally　accelerating　ageing　method.　The　basic　quantitative　evaluation　model　has　been　proposed　by　partial　least　squares　(PLS)　coupled　with　Savitzky-Golay　(S-G)　convolution　method.　Further,　three　modified　evaluation　models　by　combining　the　outlier　sample　eliminating　algorithm　based　on　joint　XY　distances　with　the　competitive　adaptive　reweighted　sampling　(CARS)　were　introduced　to　optimize　the　models.　The　assessments　of　predictive　results　suggest　that　the　quantitative　evaluation　model　established　by　XY　eliminating　algorithm　coupled　with　CARS　could　self-adaptively　predict　the　ageing　condition　of　oil-immersed　paper,　with　less　spectral　data　but　achieving　higher　accuracy.　The　results　of　field　test　and　assessment　on　five　power　transformers　show　that　the　established　model　has　good　performance　in　predicting　the　aging　state　of　oil-immersed　paper.　©　2019　Chin.　Soc.　for　Elec.　Eng.