7. The device according to claim 6 , wherein the second processing unit comprises: a first processing sub-unit, configured to collect a variety of texts; and a second processing sub-unit, configured to carry out the word segmentation processing for each of the texts, and extract all unrepeated words obtained by the word segmentation processing as the characteristic words to form a characteristic word list; or, remove high-frequency words, stop words and low-frequency words from all the unrepeated words obtained by the word segmentation processing, and consider remaining words as the characteristic words to form a characteristic word list; for each characteristic word y and each hyperlink word x, calculating the co-occurrence frequency P(x/y) using a formula of: P(x/y)=a quantity of xy co-occurrences/a quantity of y occurrences; wherein, the quantity of xy co-occurrences represents the number of texts appearing the characteristic words y and the hyperlink words x in the same time; the quantity of y occurrences represents the number of texts appearing the characteristic words y; or, for each characteristic word y and each hyperlink word x, calculating the co-occurrence frequency P(x/y) using a formula of: P(x/y)=H(x,y)/l(x,y)=H(x,y)/(H(x)+H(y)−H(x,y)), wherein, H represents information entropy; I represents mutual information.
7. The device according to claim 6 , wherein the second processing unit comprises: a first processing sub-unit, configured to collect a variety of texts; and a second processing sub-unit, configured to carry out the word segmentation processing for each of the texts, and extract all unrepeated words obtained by the word segmentation processing as the characteristic words to form a characteristic word list; or, remove high-frequency words, stop words and low-frequency words from all the unrepeated words obtained by the word segmentation processing, and consider remaining words as the characteristic words to form a characteristic word list; for each characteristic word y and each hyperlink word x, calculating the co-occurrence frequency P(x/y) using a formula of: P(x/y)=a quantity of xy co-occurrences/a quantity of y occurrences; wherein, the quantity of xy co-occurrences represents the number of texts appearing the characteristic words y and the hyperlink words x in the same time; the quantity of y occurrences represents the number of texts appearing the characteristic words y; or, for each characteristic word y and each hyperlink word x, calculating the co-occurrence frequency P(x/y) using a formula of: P(x/y)=H(x,y)/l(x,y)=H(x,y)/(H(x)+H(y)−H(x,y)), wherein, H represents information entropy; I represents mutual information. 8. The device according to claim 7 , wherein the adding module comprises: a third processing unit, configured to carry out the word segmentation processing to the text X and obtain a segmentation result; and a fourth processing unit, configured to extract the hyperlink words occurred in the hyperlink word list and the characteristic words occurred in the characteristic word list from the segmentation result, compute a weight of each of the hyperlink words that are occurred in the hyperlink word list, and compute a weight of each of the characteristic words that are occurred in the characteristic word list, and determine the final weight of each of the hyperlink word according to each co-occurrence frequency and the weights of the hyperlink words; and a fifth processing unit, configured to descendingly sort the hyperlink words occurred in the hyperlink word list according to the final weights of the hyperlink words, and obtain K numbers of hyperlink words that are arranged in first, and add hyperlinks to the K numbers of hyperlink words, wherein, K is a positive integer.