To solve the problems of complexity and large errors in oil aging analysis at the present stage, a technique integrating infrared spectroscopy and machine learning is proposed. With the help of a Fourier-Transform Mid-Infrared (FT-MIR) spectrometer, the sample spectra of three kinds of transformer oils were collected at different aging times. Various preprocessing methods were used to preprocess the sample spectra, and then the peaks were automatically sought and the sum of the characteristic peak areas was obtained. PLSR and PSO-SVR were used to establish a quantitative analysis model of transformer oil aging degree, and the effects of multiple spectral data preprocessing methods on the processing effects of infrared spectral noise reduction and baseline correction, as well as on the quantitative analysis effects of two models were investigated and analyzed. The results show that the best oil spectral preprocessing is the smoothing method, in which the SG+SVR and SG+PLSR model fitting Goodness-of-Fit (R²) are 98.14% and 99.13%, respectively, and the mean absolute error (MAE) is 0.312 4 and 0.288 0, and the root-mean-square error (RMSE) is only 0.097 7 and 0.379 0. Under the appropriate preprocessing conditions, both machine learning algorithms are robust and reliable, and the difference between the predicted and actual values of the models is extremely small.