A search intention identifying method. The method includes: at a device having one or more processor and memory, obtaining a to-be-identified query character string, and obtaining a history web page set corresponding to the query character string, the history web page set comprising web pages clicked by using the query character string historically; obtaining a predetermined web page categorization model; obtaining a category of each web page in the history web page set according to the web page categorization model; collecting statistics on the number of web pages in each category in the history web page set, and performing calculation according to the number of the web pages in each category and a total number of web pages in the history web page set to obtain intention distribution of the query character string; and obtaining an intention identification result of the query character string according to the intention distribution.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A search intention identifying method, comprising: at a device having one or more processor and memory, obtaining a to-be-identified query character string, and obtaining a history web page set corresponding to the query character string, the history web page set comprising web pages clicked by using the query character string historically; obtaining a predetermined web page categorization model; obtaining a category of each web page in the history web page set according to the web page categorization model; collecting statistics on the number of web pages in each category in the history web page set, and performing calculation according to the number of the web pages in each category and a total number of web pages in the history web page set to obtain intention distribution of the query character string; and obtaining an intention identification result of the query character string according to the intention distribution. 2. The method according to claim 1, further comprising: obtaining a character string categorization model, and obtaining a predicted category of the query character string according to the character string categorization model, wherein the obtaining an intention identification result of the query character string according to the intention distribution further includes: obtaining the intention identification result of the query character string according to the intention distribution and the predicted category of the query character string. 3. The method according to claim 2, wherein, before obtaining a character string categorization model, the method further comprises: obtaining a query character string corresponding to a category having a largest intention probability in intention distribution corresponding to a history query character string, and using the query character string as a category training query character string, wherein the category having a largest intention probability comprises multiple different categories; extracting at least one of a word-based n-gram feature and a character-based n-gram feature for category training query character strings corresponding to the different categories, wherein n is an integer greater than 1 and less than a word length or character length of a currently extracted query character string; and using the n-gram feature and a corresponding category as training data, and performing training by using a categorization model to generate the character string categorization model. 4. The method according to claim 1, wherein the web page categorization model is determined by a web page training method comprising: obtaining a training web page set having a plurality of web pages and with manually annotated categories; obtaining an effective history query character string of a first training web page in the training web page set, and performing word segmentation on the effective history query character string; obtaining an effective number of times of each segmented word, the effective number of times being a total number of times the segmented word occurs in the effective history query character string; calculating a segmented-word weight of each segmented word according to the effective number of times of each segmented word; generating a web page vector of the first training web page according to each segmented word and the corresponding segmented-word weight; generating web page vectors for remaining training web pages in the training web page set; and generating a web page categorization model according to the manually annotated categories of the web pages in the training web page set and the corresponding web page vectors. 5. The method according to claim 4, the web page training method further comprising: obtaining a Latent Dirichlet Allocation (LDA) feature of each web page in the training web page set, wherein the generating a web page categorization model according to the manually annotated categories of the web page in the training web page set and the corresponding web page vectors further includes: generating the web page categorization model according to the LDA features of the web pages, the manually annotated categories, and the corresponding web page vectors. 6. The method according to claim 4, wherein the generating a web page categorization model according to the manually annotated categories of the web page in the training web page set and the corresponding web page vectors further includes: substituting the manually annotated categories of the web pages in the training web page set and the corresponding web page vectors into a logistic regression (LR) model and performing training to obtain the web page categorization model. 7. The method according to claim 4, wherein the calculating a segmented-word weight of each segmented word according to the effective number of times of each segmented word comprises: calculating a segmented-word weight W(qi) of a segmented word qi according to a formula W(qi)=log(ci+1), wherein i is a sequence number of the segmented word, and ci is an effective number of times of the segmented word qi. 8. A non-transitory computer-readable storage medium containing computer-executable instructions for, when executed by one or more processors, performing a search intention identifying method, the method comprising: obtaining a to-be-identified query character string, and obtaining a history web page set corresponding to the query character string, the history web page set comprising web pages clicked by using the query character string historically; obtaining a predetermined web page categorization model; obtaining a category of each web page in the history web page set according to the web page categorization model; collecting statistics on the number of web pages in each category in the history web page set, and performing calculation according to the number of the web pages in each category and a total number of web pages in the history web page set to obtain intention distribution of the query character string; and obtaining an intention identification result of the query character string according to the intention distribution. 9. The non-transitory computer-readable storage medium according to claim 8, the method further comprising: obtaining a character string categorization model, and obtaining a predicted category of the query character string according to the character string categorization model, wherein the obtaining an intention identification result of the query character string according to the intention distribution further includes: obtaining the intention identification result of the query character string according to the intention distribution and the predicted category of the query character string. 10. The non-transitory computer-readable storage medium according to claim 9, wherein, before obtaining a character string categorization model, the method further comprises: obtaining a query character string corresponding to a category having a largest intention probability in intention distribution corresponding to a history query character string, and using the query character string as a category training query character string, wherein the category having a largest intention probability comprises multiple different categories; extracting at least one of a word-based n-gram feature and a character-based n-gram feature for category training query character strings corresponding to the different categories, wherein n is an integer greater than 1 and less than a word length or character length of a currently extracted query character string; and using the n-gram feature and a corresponding category as training data, and performing training by using a categorization model to generate the character string categorization model. 11. The non-transitory computer-readable storage medium according to claim 8, further containing computer-executable instructions for, when executed by one or more processors, performing a web page training method for determining the web page categorization model, the web page training method comprising: obtaining a training web page set having a plurality of web pages and with manually annotated categories; obtaining an effective history query character string of a first training web page in the training web page set, and performing word segmentation on the effective history query character string; obtaining an effective number of times of each segmented word, the effective number of times being a total number of times the segmented word occurs in the effective history query character string; calculating a segmented-word weight of each segmented word according to the effective number of times of each segmented word; generating a web page vector of the first training web page according to each segmented word and the corresponding segmented-word weight; generating web page vectors for remaining training web pages in the training web page set; and generating a web page categorization model according to the manually annotated categories of the web pages in the training web page set and the corresponding web page vectors. 12. The non-transitory computer-readable storage medium according to claim 11, the web page training method further comprising: obtaining a Latent Dirichlet Allocation (LDA) feature of each web page in the training web page set, wherein the generating a web page categorization model according to the manually annotated categories of the web page in the training web page set and the corresponding web page vectors further includes: generating the web page categorization model according to the LDA features of the web pages, the manually annotated categories, and the corresponding web page vectors. 13. The non-transitory computer-readable storage medium according to claim 11, wherein the generating a web page categorization model according to the manually annotated categories of the web page in the training web page set and the corresponding web page vectors further includes: substituting the manually annotated categories of the web pages in the training web page set and the corresponding web page vectors into a logistic regression (LR) model and performing training to obtain the web page categorization model. 14. The non-transitory computer-readable storage medium according to claim 11, wherein the calculating a segmented-word weight of each segmented word according to the effective number of times of each segmented word comprises: calculating a segmented-word weight W(qi) of a segmented word qi according to a formula W(qi)=log(ci+1), wherein i is a sequence number of the segmented word, and ci is an effective number of times of the segmented word qi. 15. A search intention identifying device, comprising: a memory for storing program instructions; a processor coupled to the memory, the processor being configured to execute the program instructions for: obtaining a to-be-identified query character string, and obtaining a history web page set corresponding to the query character string, the history web page set comprising web pages clicked by using the query character string historically; obtaining a predetermined web page categorization model; obtaining a category of each web page in the history web page set according to the web page categorization model; collecting statistics on the number of web pages in each category in the history web page set, and performing calculation according to the number of the web pages in each category and a total number of web pages in the history web page set to obtain intention distribution of the query character string; and obtaining an intention identification result of the query character string according to the intention distribution. 16. The device according to claim 15, wherein the processor is configured to execute the program instructions for: obtaining a character string categorization model, and obtaining a predicted category of the query character string according to the character string categorization model, wherein the obtaining an intention identification result of the query character string according to the intention distribution further includes: obtaining the intention identification result of the query character string according to the intention distribution and the predicted category of the query character string. 17. The device according to claim 16, wherein the processor is configured to execute the program instructions for, before obtaining a character string categorization model: obtaining a query character string corresponding to a category having a largest intention probability in intention distribution corresponding to a history query character string, and using the query character string as a category training query character string, wherein the category having a largest intention probability comprises multiple different categories; extracting at least one of a word-based n-gram feature and a character-based n-gram feature for category training query character strings corresponding to the different categories, wherein n is an integer greater than 1 and less than a word length or character length of a currently extracted query character string; and using the n-gram feature and a corresponding category as training data, and performing training by using a categorization model to generate the character string categorization model. 18. The device according to claim 15, wherein the processor is configured to determine the web page categorization model by a web page training method comprising: obtaining a training web page set having a plurality of web pages and with manually annotated categories; obtaining an effective history query character string of a first training web page in the training web page set, and performing word segmentation on the effective history query character string; obtaining an effective number of times of each segmented word, the effective number of times being a total number of times the segmented word occurs in the effective history query character string; calculating a segmented-word weight of each segmented word according to the effective number of times of each segmented word; generating a web page vector of the first training web page according to each segmented word and the corresponding segmented-word weight; generating web page vectors for remaining training web pages in the training web page set; and generating a web page categorization model according to the manually annotated categories of the web pages in the training web page set and the corresponding web page vectors. 19. The device according to claim 18, wherein the web page training method further comprising: obtaining a Latent Dirichlet Allocation (LDA) feature of each web page in the training web page set, wherein the generating a web page categorization model according to the manually annotated categories of the web page in the training web page set and the corresponding web page vectors further includes: generating the web page categorization model according to the LDA features of the web pages, the manually annotated categories, and the corresponding web page vectors. 20. The device according to claim 18, wherein the generating a web page categorization model according to the manually annotated categories of the web page in the training web page set and the corresponding web page vectors further includes: substituting the manually annotated categories of the web pages in the training web page set and the corresponding web page vectors into a logistic regression (LR) model and performing training to obtain the web page categorization model.