Trans fatty acids (TFA) in oils and fats are harmful to people’s health, so it is necessary to monitor their content. In this research, 79 samples of edible vegetable oils were collected, involving 9 varieties and 27 brands. The number of samples that were allocated to calibration and prediction sets was 53 and 26, respectively. Raman spectra of 79 edible vegetable oil samples were collected by a QE65000 Raman spectrometer, and adaptive iteratively reweighted penalized least squares was used to remove fluorescence background of Raman spectra. Then, various normalization methods were used to process Raman spectra, and preliminary selection of modeling wavenumber range of Raman spectra was carried out. After that, competitive adaptive reweighted sampling (CARS) method was used to select TFA-related variables, and partial least squares regression was used to correlate the spectral intensity of TFA characteristic variables with the real content determined by gas chromatography to establish quantitative prediction model of TFA content in edible vegetable oils. The results indicate that among various normalization methods, four normalization methods can improve the performance of PLS quantitative prediction model, and area normalization method has the best effect. After primary selection of wavenumber range, the range of wavenumber is reduced from 686 to 2 301 cm-1 to 737 to 1 787 cm-1, and the optimum range of wavenumber is determined to be 737 to 1 787 cm-1. Thirty-one spectral variables are selected by CARS method. The selected spectral variables are mainly distributed near the Raman vibration peaks of 1 265, 1 303, 1 442 and 1 658 cm-1, and the variables in the both sides of the Raman vibration peaks of 974 cm-1 are also selected. In addition, the PLS modeling results of CARS method were better than those of the commonly used methods such as uninformative variable elimination and successive projections algorithm. Therefore, it is feasible to detect TFA content in edible vegetable oil by laser Raman spectroscopy combined with chemometrics. Normalization method, wavenumber range selection and CARS method can effectively improve the prediction accuracy and stability of TFA quantitative prediction model. The correlation coefficients and root mean square errors of optimized TFA quantitative prediction model in calibration and prediction sets are 0.949, 0.953 and 0.188%, 0.191%, respectively. Compared with the unoptimized prediction model, the root mean square error of prediction decreases from 0.361% to 0.191%, with a decrease of 47.1%. The number of variables used in modeling decreases from 683 to 31, accounting for only 4.54% of the original variables.