A banknote management method, comprising the following steps of: collecting, identifying and processing, by a banknote information processing apparatus, a banknote feature to obtain banknote feature information; transmitting the banknote feature information, service information, and information of the banknote information processing apparatus together to a master server; and integrating, by the master server, the banknote feature information, the service information and the information of the banknote information processing apparatus received, and classifying banknotes, wherein the identifying the banknote feature specifically comprises following steps of: extracting a grayscale image of a region where the banknote feature is located, and performing edge detection on the grayscale image; rotating the image; positioning single numbers in the image, which specifically comprises: performing binarization processing on the image through adaptive binarization to obtain a binarized image; then projecting the binarized image; and finally segmenting the numbers by setting a moving window and using a manner of moving window registration to obtain an image of each number; characterized by performing lasso on characters contained in the image of each number, and performing normalization on the image of each number, preferably, the normalization comprising size normalization and brightness normalization; and identifying the image of the normalized number using a neural network to obtain the banknote feature, preferably, the banknote feature being a prefix number. collecting, identifying and processing, by a banknote information processing apparatus, a banknote feature to obtain banknote feature information; transmitting the banknote feature information, service information, and information of the banknote information processing apparatus together to a master server; and integrating, by the master server, the banknote feature information, the service information and the information of the banknote information processing apparatus received, and classifying banknotes, wherein the identifying the banknote feature specifically comprises following steps of: extracting a grayscale image of a region where the banknote feature is located, and performing edge detection on the grayscale image; rotating the image; positioning single numbers in the image, which specifically comprises: performing binarization processing on the image through adaptive binarization to obtain a binarized image; then projecting the binarized image; and finally segmenting the numbers by setting a moving window and using a manner of moving window registration to obtain an image of each number; characterized by performing lasso on characters contained in the image of each number, and performing normalization on the image of each number, preferably, the normalization comprising size normalization and brightness normalization; and identifying the image of the normalized number using a neural network to obtain the banknote feature, preferably, the banknote feature being a prefix number. extracting a grayscale image of a region where the banknote feature is located, and performing edge detection on the grayscale image; rotating the image; positioning single numbers in the image, which specifically comprises: performing binarization processing on the image through adaptive binarization to obtain a binarized image; then projecting the binarized image; and finally segmenting the numbers by setting a moving window and using a manner of moving window registration to obtain an image of each number; characterized by performing lasso on characters contained in the image of each number, and performing normalization on the image of each number, preferably, the normalization comprising size normalization and brightness normalization; and identifying the image of the normalized number using a neural network to obtain the banknote feature, preferably, the banknote feature being a prefix number.