As the problem of marine oil spills becomes more and more serious, a variety of remote sensing technologies are used to monitor oil spills on the sea surface. Among them, Laser-Induced Fluorescence (LIF) technology is considered one of the most effective oil spill detection technologies. Based on LIF technology, Hoge et al. proposed an integral inversion algorithm based on Raman scattering light to evaluate the thickness of thin oil film, which has been widely used in oil spill detection on the sea surface. Given the large error of the algorithm, an inversion algorithm for evaluating the thickness of oil spills on the sea surface is proposed by using Raman scattering light and fluorescence signals. Firstly, the oil film thickness is inversed by Raman scattering light signal, and then the fluorescence feature spectrum of oil is calculated using the inversion result, and finally, the oil film thickness is inversed by using the fluorescence signal. The algorithm for inversion of oil film thickness using fluorescence signal is deduced, the approximation algorithm of oil fluorescence feature spectrum and the error analysis of oil film thickness inversion using fluorescence signal is given. Experiments verify the method, Crude oil and diesel are selected as the experimental oil and the laser with wavelength of 405 nm is used as the excitation source. The collection wavelength range is 420~700 nm. The background fluorescence and Raman scattering spectra of sea water, the fluorescence spectra of 2, 5, 10 and 20 μm oil films are collected. The collected data are divided into a training set and a test set. The fluorescence feature spectrum of the oil is obtained by gradient descent method using the training set data, and the oil film thickness is retrieved by the Raman integration method and the method in this paper respectively, using the test set. Using Raman integral method, the average errors for different thicknesses of crude oil are 12.6%, 4.6%, 4.4% and 2.3%, and the average errors for different thicknesses of diesel oil are 14.0%, 7.0%, 4.2% and 3.6%; Using this method, the average errors for different thickness of crude oil are 2.5%, 2.2%, 1.2% and 1.1%, and the average errors for different thickness of diesel oil are 3.0%, 2.4%, 2.7% and 1.6%. The experimental results show that the errors of the 2 μm oil film inversion results are reduced the most. The errors of the 2 μm oil film inversion results for crude oil and diesel oil are reduced from 12.6% and 14.0% to 2.5% and 3.0%. The errors of the oil film inversion results of other thicknesses are also greatly reduced. The errors of the oil film thickness inversion results are all less than 3%. The algorithm can effectively improve the accuracy of the oil film thickness inversion results.