Motor oil pollutants have a non-negligible impact on crop growth and soil matrix, causing phenomena such as crop yield reduction and even crop failure. In order to solve the problem of predicting the concentration of motor oil pollutants in the soil surface layer, the fluorescence induction technique was used to obtain the spectral curves of motor oil, and the method of predicting the concentration of pollutant oils in the soil surface layer using the wavelet kurtosis as a quantitative parameter was proposed, and a comparative analysis was carried out by combining the three different kinds of motor oils on the market with the random forest regression algorithm. The experimental results show that the concentration prediction results of random forest with the selected wavelet kurtosis parameter for the three kinds of motor oils are evaluated using the correlation coefficient R_P and the root mean square deviation (RMSD), and the prediction of gear oil, engine oil, and motorcycle oil are improved by 1.2%, 2.2%, and 1.9%, and 14.9%, 32.4%, and 16.8%, respectively. Among the experimentally prepared samples of three kinds of engine oils, from which 30 groups of samples with concentrations of 0.01 ~ 0.3 mL/g each are selected for model prediction validation, the recognition accuracy of which is improved by 6.67%, 6.66%, 9.96%, respectively. It is also verified that the prediction accuracy of the wavelet kurtosis parameter is improved in several regression models with high prediction performance. The research results provide a certain reference for the regression model for the prediction of the concentration of other hydrocarbon pollutants in the soil surface layer, and provides an effective detection means for the sustainable development of agricultural production and soil environment.