4. The method according to claim 1 , wherein, in the recognition stage, firstly, cargoes in a container are scanned, and a scanned image is pre- segmented to generate several small. regions each being relatively consistent in terms of gray scale and texture; subsequently, features of the small regions are extracted, and the small regions are recognized by using a classifier generated by means of training according to the extracted features, to obtain a probability of each small region pertaining to a certain category of cargoes; finally, a probabilistic graphic model is constructed by using the probabilities and correlations between adjacent small regions, and the small regions are merged to obtain large regions each representing a category, thereby completing cargo classification.
4. The method according to claim 1 , wherein, in the recognition stage, firstly, cargoes in a container are scanned, and a scanned image is pre- segmented to generate several small. regions each being relatively consistent in terms of gray scale and texture; subsequently, features of the small regions are extracted, and the small regions are recognized by using a classifier generated by means of training according to the extracted features, to obtain a probability of each small region pertaining to a certain category of cargoes; finally, a probabilistic graphic model is constructed by using the probabilities and correlations between adjacent small regions, and the small regions are merged to obtain large regions each representing a category, thereby completing cargo classification. 6. The method according to claim 4 , wherein, in the recognition stage, a table of possible maximum gray scales and minimum gray scales against different thicknesses for each category of cargoes is constructed; and a minimum possible weight and a maximum possible weight of a cargo are obtained by reference to a gray scale of an image and the table of maximum gray scales and minimum gray scales.