This study focuses on selected soil samples containing different types of lubricating oil, engine oil, diesel oil, and gasoline. Three-dimensional (3D) fluorescence spectra are extracted from different soil samples, and 7 characteristic parameters are calculated for each of them, including the fluorescence intensity mean, standard deviation, transverse and longitudinal coordinates of center of gravity, correlation coefficient, long-axis slope, skewness, and kurtosis. Spectral data are used as identification characteristics for oil. Principal component analysis (PCA) is performed on the 7 characteristic parameters, and the feature vectors of the first 3 principal components after dimension reduction are extracted, accounting for a cumulative contribution rate of 88.79%. Clustering analysis reveals highly similar principal components of 5w-40 and 15w-40 lubricating oils; therefore, these oils can not be accurately classified. Subsequently, the first 3 principal components obtained by PCA are input into the back-propagation artificial neural network and the types of petroleum organic matter are used as outputs for oil identification, resulting in a 95.6% comprehensive recognition rate. Experimental results demonstrate the feasibility of identifying oil pollutants directly using 3D fluorescence spectroscopy of oily soil. Additionally, technical support is provided for subsequent research on oil pollutant identification in soil based on 3D fluorescence spectroscopy, indicating good application prospects.