Offshore oil spill accidents cause a great waste of oil resources and seriously threaten the ecological environment. Therefore, it is important to use fluorescence spectroscopy to detect oil film thickness quickly and nondestructively for effective evaluation of oil spills. Based on laser-induced fluorescence (LIF) technology, the fluorescence spectra of oil film of 0# diesel oil and 5# white oil on sea water surface were detected, and then the oil film thickness was quantified. Firstly, SG was used to preprocess the original spectral data to reduce the background noise in the original spectrum. Then, interval random frog (IRF) combined with iteratively variable subset optimization (IVSO) was used to select the wavelength of the obtained full spectral data to eliminate redundant variables. The characteristic wavelength of the spectrum screened out twice was used as the independent variable input data of partial least squares regression (PLS) to establish the oil film thickness inversion model. In the first step of the method, the characteristic bands are screened from the full spectral data by IRF, and the characteristic wavelength variables are further screened by the combination of characteristic spectral bands by IVSO to effectively improve the prediction ability and stability of the oil film thickness inversion model based on the selected characteristic wavelengths. IRF-IVSO was compared with four wavelength optimization methods: full spectrum and moving window partial least squares (MWPLS), interval random frog (IRF), variables combination population analysis (VCPA) and iteratively variable subset optimization (IVSO). The characteristic wavelengths of 0# diesel oil and 5# white oil screened by IRF-IVSO accounted for 4.48% and 19.40% of the total spectral data, respectively. The full spectrum and the characteristic wavelengths screened by the above wavelength optimization method were used as input to establish a PLS model for analysis and discussion. The results show that the prediction ability and efficiency of different models established by using the feature wavelength selection method combined with PLS are significantly higher than that of the full spectrum. Among them, the oil film thickness inversion model established by IRF-IVSO combined with PLS has the best prediction effect. This model can realize effective inversion of 0# diesel oil and 5# white oil with the thickness of 0.141 5～2.291 8 and 0.052～0.980 mm, respectively, and the correlation coefficient RP of diesel oil film test set can reach 0.961 1. The RMSEP of the test set is 0.137 5, the correlation coefficient RP of the white oil film test set is 0.971 2, and the RMSEP of the test set is 0.079 0. This study shows that IRF-IVSO can effectively and stably screen characteristic wavelength variables by combining interval band screening and single variable selection, and the oil film thickness inversion model established by combining PLS can achieve reliable prediction.