An accurate evaluation of the aging state of wire insulation materials can be used to reduce fire incidences caused by wire insulation aging. In this study, Raman spectrum detection platform, self built aging equipment, accelerated temperature aging and accelerated UV aging tests were applied to evaluate the aging state of 13 kinds of wire insulation materials(polyvinylidene-fluoride,polypropylene,polytetrachloroethylene,nylon,Yada-nylon,polyurethane,latex,perfluoroethylene-propylene-resin,rubber,polyethylene,polyvinyl-chloride). The samples were tested regularly based on temperature aging for 10 time periods. Using 32 hours interval and 15 sample data per aging time, the spectral data of 150 samples of each material (aged) were obtained. Similarly, 13 time periods of UV aging, at a time interval of 16 hours and 15 samples data per aging time, spectral data of 195 UV aging samples were recorded. According to aging period, temperature aging is divided into 10 categories, and UV aging was divided into 13 categories. Linear regression classification and a support vector machine was used to classify the original spectral data. It was found that nylon, polyurethane, Teflon, rubber, etc., have more than 80% accuracy of the two classification algorithms. However, the classification accuracy of some materials was less than 70%. The support vector machine classification of original spectral data consumed a longer time due to alarge number of samples and high spectral dimension. In order to further improve the classification accuracy and speed, the original spectral data were preprocessed by iterative adaptive weighted penalty least square method and five-point cubic smoothing. PCA compression was used to reduce the sample spectral dimension from 2048 to 3.Because the spectral dimension of the reduced sample is less than the number of samples, it can not meet the requirements of linear regression classification.So support vector machine was used for classification. After preprocessing and feature extraction, the classification effect of data was greatly improved, and the classification accuracy of temperature aging and UV aging of all the materials was more than 90%. Furthermore, the classification speed of the support vector machines has also been greatly improved. These results provide a theoretical basis for the effective evaluation of the aging state of wire insulation materials and provide technical support for preventing accidents caused by insulation aging.