1. A computer-implemented method for text classification comprising: segmenting content of a text into multiple words; determining a sum of a plurality of word vectors of an individual word of the multiple words at a first level vector space of a vector space group that includes a plurality of vector spaces, at least one of the plurality of vector spaces comprising one or more direct child classes that are non-leaf child classes in a tree class structure, the first level vector space having a plurality of classes, an individual word vector of the plurality word vectors including word frequency vectors of a corresponding word of the multiple words at multiple classes of the vector space; and classifying the text into a class of the plurality of classes that has a shortest distance to the sum of the plurality of word vectors of the word vectors.
1. A computer-implemented method for text classification comprising: segmenting content of a text into multiple words; determining a sum of a plurality of word vectors of an individual word of the multiple words at a first level vector space of a vector space group that includes a plurality of vector spaces, at least one of the plurality of vector spaces comprising one or more direct child classes that are non-leaf child classes in a tree class structure, the first level vector space having a plurality of classes, an individual word vector of the plurality word vectors including word frequency vectors of a corresponding word of the multiple words at multiple classes of the vector space; and classifying the text into a class of the plurality of classes that has a shortest distance to the sum of the plurality of word vectors of the word vectors. 8. The method as recited in claim 1 , wherein classifying the text into the class that has the shortest distance to the sum of the word vectors comprises: determining an average value of corresponding word frequency vectors for each of the multiple words at the plurality of classes; determining whether a highest value word frequency vector is higher than a second pre-defined filtering threshold for each of one or more of the multiple words associated with a corresponding average value that is higher than a first pre-defined filtering threshold; determining a sum of the corresponding word frequency vectors for each of the one or more words with a highest value word frequency vector that is higher than the second pre-defined filtering threshold; and classifying the text into a class associated with a sum of the corresponding word frequency vectors that has the highest value.