10. An apparatus for word mining and evaluating, comprising: one or more processors; a memory; and one or more program units stored in the memory and to be executed by the one or more processors, the one or more program units comprising; a DF calculating unit, configured to calculate a DF of a word in mass categorized data; a single-aspect evaluating unit, configured to evaluate the word in multiple single-aspects according to the DF of the word; a multiple-aspect evaluating unit, configured to evaluate the word in a multiple-aspect according to the multiple single-aspect evaluations to obtain an importance weight of the word, wherein the multiple-aspect evaluating unit comprises: a level dividing module, configured to configure levels according to DFs of candidate words, wherein the levels comprises a SuperHigh level, a MidHigh level, a MidLow level and a SuperLow level; and a multiple-aspect evaluating module, configured to evaluate the word in the multiple-aspect according to the level of the word to obtain an importance weight of the word in the level, to determine, for each candidate word in the SuperHigh level, the MidHigh level or the MidLow level, the importance weight of the candidate word according to: an absolute value of a difference between an average inverse document frequency (AVAIDF) and an inverse document frequency (IDF) of the candidate word, a linear combination of mutual information (MI), expect cross entropy (ECE) and entropy (ENT) of the candidate word, a combination of logarithmic normalized chi-square and information gain (IG) of the candidate word, and logarithmic normalized selective preference (SELPRE) of the candidate word; determine, for each candidate word in the SuperLow level, the importance weight of the candidate word according to, an absolute value of a difference between an average inverse document frequency (AVAIDF) and an inverse document frequency (IDF) of the candidate word, a linear combination of mutual information (MI), expect cross entropy (ECE) and entropy (ENT) of the candidate word, and a combination of logarithmic normalized chi-square and information gain (iq) of the candidate word.
10. An apparatus for word mining and evaluating, comprising: one or more processors; a memory; and one or more program units stored in the memory and to be executed by the one or more processors, the one or more program units comprising; a DF calculating unit, configured to calculate a DF of a word in mass categorized data; a single-aspect evaluating unit, configured to evaluate the word in multiple single-aspects according to the DF of the word; a multiple-aspect evaluating unit, configured to evaluate the word in a multiple-aspect according to the multiple single-aspect evaluations to obtain an importance weight of the word, wherein the multiple-aspect evaluating unit comprises: a level dividing module, configured to configure levels according to DFs of candidate words, wherein the levels comprises a SuperHigh level, a MidHigh level, a MidLow level and a SuperLow level; and a multiple-aspect evaluating module, configured to evaluate the word in the multiple-aspect according to the level of the word to obtain an importance weight of the word in the level, to determine, for each candidate word in the SuperHigh level, the MidHigh level or the MidLow level, the importance weight of the candidate word according to: an absolute value of a difference between an average inverse document frequency (AVAIDF) and an inverse document frequency (IDF) of the candidate word, a linear combination of mutual information (MI), expect cross entropy (ECE) and entropy (ENT) of the candidate word, a combination of logarithmic normalized chi-square and information gain (IG) of the candidate word, and logarithmic normalized selective preference (SELPRE) of the candidate word; determine, for each candidate word in the SuperLow level, the importance weight of the candidate word according to, an absolute value of a difference between an average inverse document frequency (AVAIDF) and an inverse document frequency (IDF) of the candidate word, a linear combination of mutual information (MI), expect cross entropy (ECE) and entropy (ENT) of the candidate word, and a combination of logarithmic normalized chi-square and information gain (iq) of the candidate word. 11. The apparatus of claim 10 , wherein the DF calculating unit comprises: a DF vector calculating module, configured to calculate a DF vector of the word in each category of the categorized data; and a DF calculating module, configured to take a sum of DF vectors of the word as the DF of the word in all the categories.