1. A word mining and evaluating method, the method comprising: calculating a Document Frequency (DF) of a word in mass categorized data; evaluating the word in multiple single-aspects according to the DF of the word; and evaluating the word in a multiple-aspect according to the evaluations in the multiple single-aspects to obtain an importance weight of the word; wherein the evaluating the word in a multiple-aspect according to the evaluations in the multiple single-aspects to obtain the importance weight of the word comprises, classifying candidate words into levels according to DFs of the candidate words, wherein the levels comprises a SuperHigh level, a MidHigh level, a MidLow level and a SuperLow level; and for each candidate word in the SuperHigh level, the MidHigh level or the MidLow level, determining 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; and for each candidate word in the SuperLow level, determining 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 (IG) of the candidate word.
1. A word mining and evaluating method, the method comprising: calculating a Document Frequency (DF) of a word in mass categorized data; evaluating the word in multiple single-aspects according to the DF of the word; and evaluating the word in a multiple-aspect according to the evaluations in the multiple single-aspects to obtain an importance weight of the word; wherein the evaluating the word in a multiple-aspect according to the evaluations in the multiple single-aspects to obtain the importance weight of the word comprises, classifying candidate words into levels according to DFs of the candidate words, wherein the levels comprises a SuperHigh level, a MidHigh level, a MidLow level and a SuperLow level; and for each candidate word in the SuperHigh level, the MidHigh level or the MidLow level, determining 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; and for each candidate word in the SuperLow level, determining 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 (IG) of the candidate word. 8. The method of claim 1 , wherein the importance weight of the candidate word in the SuperHigh level or the MidHigh level is determined according to a following formula: 
 SuperHigh( w )=MidHigh( w )=Diff( w )*ProbBased( w )*Prob DF Rel( w )*SelPre( w ); the importance weight of the candidate word in the MidLow level is determined according to a following formula: 
 MidLow( w )=Diff( w )*ProbBased( w )*Prob DF Rel( w )+SelPre( w ); wherein, SuperHigh(w) denotes the importance weight of the candidate word in the SuperHigh level, MidHigh(w) denotes the importance weight of the candidate word in the MidHigh level, MidLow(w) denotes the importance weight of the candidate word in the MidLow level, Diff (w) denotes the absolute value of the difference between the average inverse document frequency and the inverse document frequency of the candidate word, ProbBased(w) denotes the linear combination of the mutual information, the expect cross entropy and the entropy of the candidate word, ProbDFRel(w) denotes the combination of the logarithmic normalized chi-square and the information gain of the candidate word, and SelPre(w) denotes the logarithmic normalized selective preference of the candidate word.