In global friction measurement， thin oil film technique can well represent the distribution of surface friction. Irradiated with ultraviolet light of a specific wavelength， the oil film with different thickness of the fluorescent color molecules will emit different brightness. Using this principle， the thickness of the oil film can be calculated by detecting the gray value of the excited fluorescent oil film.In this paper， BP neural network and extreme learning machine （ELM） neural network are used to build models to complete the prediction of the relationship between the fluorescence oil film thickness and gray level， and Hopfield neural network is used to complete the identification of the corresponding parameters. The experimental results show that the prediction errors of ELM neural network model， BP neural network model and interpolation model are 5.150%， 5.485% and 5.935%， respectively. Through Hopfield neural network identification， the parameter error rates of the influence factors such as light source power， optical distance and exposure coefficient are controlled at about 1%， which meets the requirements of practical engineering application. Compared with the traditional interpolation method， the higher precision can be obtained by using the neural network， which provides a feasible method for the study of the gray and thickness of the fluorescent oil film.