Collinear double pulse laser induced breakdown spectroscopy (DP-LIBS) is applied to quickly and quantificationally detect the content of heavy metal chromium (Cr) in edible vegetable oil. LIBS spectra of samples are collected by a two-channel high precision spectrometer, and then several spectral lines such as three atomic lines of Cr (Cr I 425.39 nm, Cr I 427.43 nm, Cr I 428.87 nm), CN molecular line (CN 421.49 nm) and Ca atomic line (Ca II 422.64 nm) are determined at wavelength range of 420-430 nm. Then, competitive adaptive reweighted sampling (CARS) method is used to select characteristic and related variables of Cr, and least squares support vector machine (LSSVM) method is used to establish calibration model using selected variables. The results show that the number of variables reduces from 132 to 10 after CARS variable selection, and the variable compression rate is 92.42%. The correlation coefficient, root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) in CARS-LSSVM calibration model are 0.9926, 5.287×10 -6 and 5.860×10 -6, respectively, and the relative error of prediction samples is 8.55%. The performance of CARS-LSSVM calibration model is superior to that of univariate calibration model and LSSVM calibration model with five variables. So it can be concluded that DP-LIBS technique is feasible to detect the content of Cr in edible vegetable oil, and CARS method can select characteristic and related variables of Cr effectively, eliminate redundant and noise variables, thus reduce the influence of matrix effect on analytical element and improve prediction accuracy of LIBS analysis.