1. A method comprising: extracting description information of multiple products; clustering the description information of the multiple products belonging to a particular model into a first text; processing the first text by segmenting the first text to one of remove from the first text one or more terms whose term frequencies are higher than a first set threshold, and remove from the first text one or more terms whose term frequencies are lower than a second set threshold; clustering, after processing the first text, first texts of products belonging to different models into a second text; applying a subject analysis to the second text to obtain one or more subjects; defining one or more names for the one or more subjects respectively; assigning a respective name of a respective subject correlated to description information of a respective product as an identifier of the respective product; and labeling the respective product by using the identifier, wherein the applying the subject analysis to the second text to obtain one or more subjects comprises: setting a number of subjects in one or more subject models; applying the subject analysis to the second text by using a text analysis method based on the one or more subject models; obtaining a number of subsets corresponding to the number of subjects from a set of terms in the second text, the number of subsets being equal to the number of subjects, a respective subset corresponding to a respective subject; and according to the respective subset that one or more terms in the description information of the products locate, correlating the description information of the products to the respective subject corresponding to the respective subset.
1. A method comprising: extracting description information of multiple products; clustering the description information of the multiple products belonging to a particular model into a first text; processing the first text by segmenting the first text to one of remove from the first text one or more terms whose term frequencies are higher than a first set threshold, and remove from the first text one or more terms whose term frequencies are lower than a second set threshold; clustering, after processing the first text, first texts of products belonging to different models into a second text; applying a subject analysis to the second text to obtain one or more subjects; defining one or more names for the one or more subjects respectively; assigning a respective name of a respective subject correlated to description information of a respective product as an identifier of the respective product; and labeling the respective product by using the identifier, wherein the applying the subject analysis to the second text to obtain one or more subjects comprises: setting a number of subjects in one or more subject models; applying the subject analysis to the second text by using a text analysis method based on the one or more subject models; obtaining a number of subsets corresponding to the number of subjects from a set of terms in the second text, the number of subsets being equal to the number of subjects, a respective subset corresponding to a respective subject; and according to the respective subset that one or more terms in the description information of the products locate, correlating the description information of the products to the respective subject corresponding to the respective subset. 5. The method as recited in claim 1 , wherein the description information includes title information, the method further comprising: determining whether the title information of the respective product includes an additional identifier in an additional identifier database that is pre-established for a particular category that the respective product belongs; and in response to determining that the title information of the respective product includes the additional identifier, obtaining the additional identifier, and using the additional identifier to label the respective product.