Near　Infrared　Spectroscopy　(NIRS),　as　one　of　analysis　technologies,　has　shown　promisingly　industrial　applications　for　significant　properties　for　recent　decades　such　as　fast　response,　preciseness,　non-intrusion　etc.　Here,　the　authors　employed　NIRS　coupled　with　a　series　of　physical　and　chemical　tests　to　assess　the　ageing　condition　of　oil-paper　insulation,　which　is　responsible　for　the　main　insulation　type　of　high-voltage　power　transformer.　Among　these　procedures,　the　data　analytic　algorithms　are　of　utmost　importance　to　determine　the　evaluation　precision.　After　plenty　of　trial-and-error,　Savitzky-Golay　(S-G)　convolution　was　finally　utilised　to　de-noise　samples　and　improve　the　spectral　data　quality.　The　competitive　adaptive　reweighted　sampling　(CARS)　was　used　to　select　the　optimal　wavelength　combination　of　NIRS,　which　is　found　able　to　fully　extract　the　effectively　spectral　information　and　reduce　dimensions　of　spectral　data.　Based　on　the　above-mentioned　techniques,　the　quantitative　analysis　model　of　NIRS　was　established　by　partial　least　squares　(PLS),　which　could　synthetically　process　the　spectral　data　and　the　degree　of　polymerisation　(DP)　of　paper　samples.　The　results　indicated　that　compared　with　the　traditional　detection　methods,　the　NIRS　analysis　is　a　powerful　and　informative　tool　to　characterise　the　condition　of　oil-paper　insulation　without　intrusion　or　damage　to　transformers.　©　2019　Institution　of　Engineering　and　Technology.All　right　reserved.