The rapid identification of invoice authenticity was studied based on laser-induced photoluminescence spectrum. First, the spectral curves of eighty invoice samples were obtained by laser-induced photoluminescence detection system, and genetic algorithm (GA) was applied to fit and separate overlapped spectral region between 566 and 669 nm by three Gaussian peaks. Spectral feature parameters extracted by GA were employed as the inputs of BP neural networks, and then an identification model was built. One hundred and four data were converted to 13 Gaussian parameters, and for authentic and false invoices the coefficients of determination (R2) were 0.997 89 and 0.996 83 and the relative standard deviations (RSD) were 0.017 052 and 0.022 362, respectively. It was showed that Gaussian fitting algorithm could not only simplify the parameters of models, but also improve the explanation of analysis models. Through comparison analysis of the results, it was found that the model, whose thirteen feature parameters and two evaluated parameters were all applied as BP inputs, was the best, and the corrected identification rates of sixty calibration samples and twenty validation samples were both 100％. So the identification method studied in the present research played a good role in the classification and identification, and offered a new approach to the rapid identification of invoice authenticity.