Metal elements in lubricating oil can directly reflect the wear status and position of the mechanical structure, and analyzing them quantitatively is an effective means of realizing fault warning and diagnosis. Based on laser-induced breakdown spectroscopy (LIBS) technology, the correlation coefficient method and threshold setting are used to narrow the range of feature wavelengths rapidly. The feature wavelengths are extracted accurately by the iterative predictive weighted partial least squares (IPW-PLS), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS) methods. Finally, based on partial least squares (PLS) method, a quantitative analysis model of metal elements in lubricating oil is established to analyze metal elements in lubricating oil quantitatively. The experimental results show that the proposed CCPS method can improve the efficiency of characteristic wavelength selection and reduce the running time by more than 50%. The correlation coefficient RP2 and root-mean-square error prediction (RMSEP) values of CCPS-IPW-PLS are 0.9945 and 25.1678 μg/g, respectively. The RP2 and RMSEP values of CCPS-UVE are 0.9790 and 52.7363 μg/g, respectively, and the RP2 and RMSEP values of CCPS-CARS are 0.9939 and 25.0996 μg/g, respectively. These results prove the accuracy and efficiency of the proposed method. The approach provides a new way to perform the rapid, portable, and accurate detection of lubricating oil.