The rapid and accurate detection of the oil-paper insulation aging state has attracted considerable attention. In this study, classification of the original Raman spectral aging state of oil-paper insulation is performed without feature extraction. First, the aging state of the insulation paper is divided into 10 categories according to the measured polymerization degree. Raman spectroscopy is performed on the oil-paper insulation samples in each aging state. Finally, 169 groups of Raman spectra are classified by the K-nearest neighbour(KNN) algorithm and integrated enhanced KNN algorithm. The results indicate that the KNN algorithm after integration enhancement has a stronger recognition ability for the original Raman spectrum, its discriminant accuracy is 98.32%, and it has better stability. It is proved that the discriminant model based on the integrated enhanced KNN algorithm accurately discriminates the original Raman spectra of oil-paper insulation. The proposed model simplifies the diagnosis of the aging state of transformer oil-paper insulation using Raman spectra and is of considerable significance for research on this topic.