摘要： Determination of toxic metal elements in oily sludge is meaningful to treatment, migration, improvement, monitoring, and repair of oily sludge, and an accurate and rapid analytical technology is urgent necessary to quantitative detect the toxic metal elements in oily sludge. In this study, a novel method based on laser-induced breakdown spectroscopy (LIBS) technique coupled with wavelet transform-random forest (WT-RF) was proposed to perform quantitative analysis of four toxic metal elements (Cu, Zn, Cr and Ni) in 16 oily sludge samples. In order to facilitate LIBS measurement, the 16 initial oily sludge samples with a water-oil mixed state were subjected to a drying treatment at 150 °C for 5 h, and then ground and passed through a 100 mesh to sift. The 16 oily sludge samples were sliced and collected LIBS spectra, and 11 samples were selected as calibration sets, and rest samples were set as test sets. The raw spectra were first preprocessed by wavelet transform (WT) method, and then the input variables for RF calibration model were selected and optimized based on variable importance. Finally, the WT-RF model with the optimal input variables was constructed to quantitative analysis four toxic metal elements concentration in the oily sludge. The predictive performance of WT-RF model was compared with the RF, partial least squares (PLS) and WT-PLS models. The results indicates that WT-RF model shows a better predictive ability than the other three models for prediction of potential toxic metal concentration in oily sludge, and the best determination coefficient (R2) value of four elements (Cu, Zn, Cr and Ni) were 0.9756, 0.9758, 0.9772, 0.9768, the root mean square error (RMSE) were 0.0358%, 0.0365%, 0.0446% and 0.0344%, and the relative standard deviation (RSD) were 0.0908, 0.0929, 0.0797 and 0.0628. Therefore, LIBS technique combined with WT-RF method is a promising method for the rapid prediction of the toxic metal elements in oily sludge.