A method includes acquiring user data of candidate users; mining a social business characteristic user in some of the candidate users according to the first social attribute data; training a classifier by using second social attribute data and second business object attribute data of the social business characteristic user; and inputting first social attribute data and first business object attribute data of a neighboring user to the classifier, and outputting a result of whether the neighboring user, in a period of time after the first period of time, is a social business characteristic user, wherein the neighboring user is a candidate user other than the social business characteristic user. The present disclosure increases the volume of associated data, and improves the accuracy of the classifier, thus improving the accuracy of identification, so that potential social business characteristic users in the first period of time can be identified.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for identifying a social business characteristic user, the method comprising: acquiring user data of candidate users, the user data including first social attribute data and first business object attribute data that are associated in a first period of time, and second social attribute data and second business object attribute data that are associated in a second period of time, the second period of time being in a period of time prior to the first period of time; mining a social business characteristic user in at least some of the candidate users according to the first social attribute data; training a classifier by using the second social attribute data and the second business object attribute data of the social business characteristic user; inputting first social attribute data and first business object attribute data of a neighboring user to the classifier; and outputting a result of whether the neighboring user, in a period of time after the first period of time, is a social business characteristic user, the neighboring user being a candidate user other than the social business characteristic user. 2. The method of claim 1, wherein the mining the social business characteristic user in at least some of the candidate users according to the first social attribute data includes: extracting, from the first social attribute data of the candidate users, a social business message related to service processing; and identifying the social business characteristic user by using the social business message. 3. The method of claim 2, wherein the identifying the social business characteristic user by using the social business message includes identifying the social business characteristic user by using the social business message according to a graph calculation. 4. The method of claim 1, wherein the training the classifier by using the second social attribute data and the second business object attribute data of the social business characteristic user includes: selecting, from the first social attribute data and the first business object attribute data of the candidate users, first social business feature data and first business object feature data that represent service processing; extracting, from the second social attribute data and the second business object attribute data of the social business characteristic user, second social business feature data and second business object feature data of a same type as the first social business feature data and the first business object feature data; and training the classifier by using the second social business feature data and the second business object feature data. 5. The method of claim 4, wherein the training the classifier by using the second social attribute data and the second business object attribute data includes performing feature transformation on the second social business feature data and the second business object feature data of the social business characteristic user. 6. The method of claim 5, wherein the feature transformation includes one or more of the following: mean transformation; variance transformation; slope transformation, and transformation of a number of crests and troughs. 7. The method of claim 4, wherein the training the classifier by using the second social attribute data and the second business object attribute data includes: calculating a similarity between the first business object feature data of the neighboring user and the first business object feature data of the social business characteristic user; and merging the first business object feature data of the neighboring user with the first business object feature data of the social business characteristic user when the similarity is greater than a preset similarity threshold. 8. The method of claim 4, wherein the selecting, from the first social attribute data and the first business object attribute data of the candidate users, first social business feature data and first business object feature data that represent service processing includes: extracting, from the first social attribute data and the first business object attribute data of the candidate users, first social business candidate data and first business object candidate data related to the service processing; sorting the first social business candidate data and the first business object candidate data according to importance; searching for a selection rule of an industry to which the candidate users belong; and selecting, in the sorted first social business candidate data and first business object candidate data, first social business feature data and first business object feature data that satisfy the selection rule. 9. The method of claim 4, wherein the inputting the first social attribute data and the first business object attribute data of the neighboring user to the classifier, and outputting the result of whether the neighboring user, in the period of time after the first period of time, is the social business characteristic user includes: inputting the first social business feature data and the first business object feature data of the neighboring user to the classifier, and outputting the result of whether the neighboring user, in a period of time after the first period of time, is a social business characteristic user. 10. The method of claim 8, wherein the inputting the first social attribute data and the first business object attribute data of the neighboring user to the classifier, and outputting the result of whether the neighboring user, in the period of time after the first period of time, is the social business characteristic user includes: performing feature transformation on the first social business feature data and the first business object feature data of the neighboring candidate user. 11. The method of claim 10, wherein the feature transformation includes one or more of the following: mean transformation; variance transformation; slope transformation; and transformation of a number of crests and troughs. 12. One or more memories stored thereon computer-executable instructions, executable by one or more processors, to cause the one or more processors to perform acts comprising: acquiring user data of candidate users, the user data including first social attribute data and first business object attribute data that are associated in a first period of time, and second social attribute data and second business object attribute data that are associated in a second period of time, the second period of time being in a period of time prior to the first period of time; mining a social business characteristic user in at least some of the candidate users according to the first social attribute data; training a classifier by using the second social attribute data and the second business object attribute data of the social business characteristic user; inputting first social attribute data and first business object attribute data of a neighboring user to the classifier; and outputting a result of whether the neighboring user, in a period of time after the first period of time, is a social business characteristic user, the neighboring user being a candidate user other than the social business characteristic user. 13. The one or more memories of claim 12, wherein the mining the social business characteristic user in at least some of the candidate users according to the first social attribute data includes: extracting, from the first social attribute data of the candidate users, a social business message related to service processing; and identifying the social business characteristic user by using the social business message. 14. The one or more memories of claim 13, wherein the identifying the social business characteristic user by using the social business message includes identifying the social business characteristic user by using the social business message according to a graph calculation. 15. The one or more memories of claim 12, wherein the training the classifier by using the second social attribute data and the second business object attribute data of the social business characteristic user includes: selecting, from the first social attribute data and the first business object attribute data of the candidate users, first social business feature data and first business object feature data that represent service processing; extracting, from the second social attribute data and the second business object attribute data of the social business characteristic user, second social business feature data and second business object feature data of a same type as the first social business feature data and the first business object feature data; and training the classifier by using the second social business feature data and the second business object feature data. 16. The one or more memories of claim 15, wherein the training the classifier by using the second social attribute data and the second business object attribute data includes performing feature transformation on the second social business feature data and the second business object feature data of the social business characteristic user, wherein the feature transformation includes one or more of the following: mean transformation; variance transformation; slope transformation; and transformation of a number of crests and troughs. 17. The one or more memories of claim 15, wherein the training the classifier by using the second social attribute data and the second business object attribute data includes: calculating a similarity between the first business object feature data of the neighboring user and the first business object feature data of the social business characteristic user; and merging the first business object feature data of the neighboring user with the first business object feature data of the social business characteristic user when the similarity is greater than a preset similarity threshold. 18. The one or more memories of claim 15, wherein the selecting, from the first social attribute data and the first business object attribute data of the candidate users, first social business feature data and first business object feature data that represent service processing includes: extracting, from the first social attribute data and the first business object attribute data of the candidate users, first social business candidate data and first business object candidate data related to the service processing; sorting the first social business candidate data and the first business object candidate data according to importance; searching for a selection rule of an industry to which the candidate users belong; and selecting, in the sorted first social business candidate data and first business object candidate data, first social business feature data and first business object feature data that satisfy the selection rule. 19. The one or more memories of claim 15, wherein the inputting the first social attribute data and the first business object attribute data of the neighboring user to the classifier, and outputting the result of whether the neighboring user, in the period of time after the first period of time, is the social business characteristic user includes: inputting the first social business feature data and the first business object feature data of the neighboring user to the classifier, and outputting the result of whether the neighboring user, in a period of time after the first period of time, is a social business characteristic user. 20. A device for identifying a social business characteristic user, the device comprising: a user data acquisition module that acquires user data of candidate users, wherein the user data comprises first social attribute data and first business object attribute data associated in a first period of time, and second social attribute data and second business object attribute data associated in a second period of time, and the second period of time is in a period of time prior to the first period of time; a social business characteristic user mining module that mines a social business characteristic user in at least some of the candidate users according to the first social attribute data; a classifier training module, that trains a classifier by using the second social attribute data and the second business object attribute data of the social business characteristic user; and a social business characteristic user identification module, that inputs first social attribute data and first business object attribute data of a neighboring user to the classifier, and output a result of whether the neighboring user, in a period of time after the first period of time, is a social business characteristic user, wherein the neighboring user is a candidate user other than the social business characteristic user.