A method for recognizing and classifying banknotes in a banknote processing device, comprising: acquiring sample information of brand new banknotes to be learned and banknote sample information to be recognized; establishing, according to a preset rule, a banknote sample signal degeneration model, wherein the banknote sample signal degeneration model comprises: i) a banknote condition degeneration model established based on linear change of image brightness, and ii) a banknote image degeneration model based on randomness of a statistic model, wherein the banknote condition degeneration model comprises signal degeneration models for banknote contamination, banknote incompletion, banknote crack, and banknote fold or deflection; inputting the sample information into the banknote sample signal degeneration model to acquire various banknote sample information corresponding to the brand new banknotes to be learned, wherein the various banknote sample information corresponding to the brand new banknotes to be learned comprises simulation data, the simulation data is calculated by using parameters of the banknote sample signal degeneration model and the inputted sample information, and the simulation data comprises at least one of gray values for pixel points after degeneration, contamination in varying degrees, incompletion in varying degrees, crack in varying degrees, and fold in a certain region; inputting the various banknote sample information to perform classifier learning, and outputting a banknote classification model; and performing sample signal preprocessing and feature extraction on the sample information to be recognized, performing classification decision on the banknote to be recognized by using the classification model, and outputting a final classification result. acquiring sample information of brand new banknotes to be learned and banknote sample information to be recognized; establishing, according to a preset rule, a banknote sample signal degeneration model, wherein the banknote sample signal degeneration model comprises: i) a banknote condition degeneration model established based on linear change of image brightness, and ii) a banknote image degeneration model based on randomness of a statistic model, wherein the banknote condition degeneration model comprises signal degeneration models for banknote contamination, banknote incompletion, banknote crack, and banknote fold or deflection; inputting the sample information into the banknote sample signal degeneration model to acquire various banknote sample information corresponding to the brand new banknotes to be learned, wherein the various banknote sample information corresponding to the brand new banknotes to be learned comprises simulation data, the simulation data is calculated by using parameters of the banknote sample signal degeneration model and the inputted sample information, and the simulation data comprises at least one of gray values for pixel points after degeneration, contamination in varying degrees, incompletion in varying degrees, crack in varying degrees, and fold in a certain region; the various banknote sample information corresponding to the brand new banknotes to be learned comprises simulation data, the simulation data is calculated by using parameters of the banknote sample signal degeneration model and the inputted sample information, and the simulation data comprises at least one of gray values for pixel points after degeneration, contamination in varying degrees, incompletion in varying degrees, crack in varying degrees, and fold in a certain region; inputting the various banknote sample information to perform classifier learning, and outputting a banknote classification model; and performing sample signal preprocessing and feature extraction on the sample information to be recognized, performing classification decision on the banknote to be recognized by using the classification model, and outputting a final classification result.