A method for conducting classification model training includes: acquiring sample feature vectors of a plurality of users according to at least one feature set for each of the users, wherein the at least one feature set for a user is determined based on at least one sample message of the user; determining gender identifiers of the users according to the sample feature vectors; and conducting training based on the sample feature vectors and the gender identifiers corresponding to the sample feature vectors to construct a gender classification model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for conducting classification model training, comprising: acquiring sample feature vectors of a plurality of users according to at least one feature set for each of the users, wherein the at least one feature set for a user is determined based on at least one sample message of the user; determining gender identifiers of the users according to the sample feature vectors; and conducting training based on the sample feature vectors and the gender identifiers corresponding to the sample feature vectors to construct a gender classification model. 2. The method of claim 1, wherein the training based on the sample feature vectors and the gender identifiers corresponding to the sample feature vectors is conducted using a decision tree algorithm to construct the gender classification model. 3. The method of claim 2, wherein conducting the training based on the sample feature vectors and the gender identifiers corresponding to the sample feature vectors by using a decision tree algorithm to construct the gender classification model comprises: (a) combining the sample feature vectors and the gender identifiers corresponding to the sample feature vectors to form a current set of feature data; (b) acquiring, at a current level, gain values of feature dimensions of the current set of feature data, wherein a feature dimension corresponds to a feature value at a corresponding position within the sample feature vectors, and a gain value corresponding to the feature dimension represents an extent to which the feature dimension affects results of gender classification; (c) determining a feature dimension within the current set of feature data that has the largest gain value as a test dimension, and constructing, at the current level, a node corresponding to the test dimension; (d) dividing the current set of feature data into at least one subset of the feature data in accordance with a feature value corresponding to the test dimension in the current set of feature data, and deleting feature values corresponding to the test dimension from the at least one subset, to obtain at least one set of feature data; (e) forwarding the at least one set of feature data to a level lower than the current level and constructing a branch node of the node at the current level according to the at least one set of feature data; (f) repeating (b)-(e) until a current set of feature data contains one kind of gender identifier; (g) constructing a node according to the gender identifier; and (h) assembling the nodes constructed at the levels to form the gender classification model. 4. The method of claim 1, further comprising: determining a gender identifier of a target user based on the gender classification model to classify the target user. 5. The method of claim 4, wherein the determining a gender identifier of the target user based on the gender classification model comprises: acquiring a target feature vector of the target user according to at least one feature set for the target user, wherein the at least one feature set for the target user is determined based on at least one target message of the target user; and determining the gender identifier of the target user according to the target feature vector and the gender classification model. 6. The method of claim 5, further comprising performing at least one of: acquiring at least one target message of the target user in each preset period of time, and determining the at least one feature set for the target user from the at least one target message; or acquiring at least one target message of the target user upon detection that a number of target messages of the target user increases by a preset threshold number, and determining the at least one feature set for the target user from the at least one target message. 7. The method of claim 1, wherein the at least one feature set comprises at least one of a salutation feature set, an operation feature set, or an application feature set. 8. The method of claim 7, wherein the salutation feature set comprises a male salutation feature set and a female salutation feature set. 9. The method of claim 7, wherein the operation feature set comprises at least one of a number of times of online shopping, a number of times participating in group-shopping, or an amount of consumption per month. 10. The method of claim 7, wherein the application feature set comprises one of a number of application APP registration or gender-specific APP. 11. A device, comprising: a processor; a memory configured to store instructions executable by the processor, wherein the processor is configured to: acquire sample feature vectors of a plurality of users according to at least one feature set for each of the users, wherein the at least one feature set for a user is determined based on at least one sample message of the user; determine gender identifiers of the users according to the sample feature vectors; and conduct training based on the sample feature vectors and the gender identifiers corresponding to the sample feature vectors to construct a gender classification model. 12. The device of claim 11, wherein the processor is further configured to conduct the training based on the sample feature vectors and the gender identifiers corresponding to the sample feature vectors by using a decision tree algorithm to construct the gender classification model. 13. The device of claim 12, wherein the processor is further configured to: (a) combine the sample feature vectors and the gender identifiers corresponding to the sample feature vectors to form a current set of feature data; (b) acquire, at a current level, gain values of feature dimensions of the current set of feature data, wherein a feature dimension corresponds to a feature value at a corresponding position within the sample feature vectors, and a gain value corresponding to the feature dimension represents an extent to which the feature dimension affects results of gender classification; (c) determine a feature dimension within the current set of feature data that has the largest gain value as a test dimension, and construct, at the current level, a node corresponding to the test dimension; (d) divide the current set of feature data into at least one subset of the feature data in accordance with a feature value corresponding to the test dimension in the current set of feature data, and delete feature values corresponding to the test dimension from the at least one subset, to obtain at least one set of feature data; (e) forward the at least one set of feature data to a level lower than the current level and construct a branch node of the node at the current level according to the at least one set of feature data; (f) repeat (b)-(e) until a current set of feature data contains one kind of gender identifier; (g) construct a node according to the gender identifier; and (h) assemble the nodes constructed at the levels to form the gender classification model. 14. The device of claim 11, wherein the processor is further configured to determine a gender identifier of a target user based on the gender classification model to classify the target user. 15. The device of claim 14, wherein the processor is further configured to: acquire a target feature vector of the target user according to at least one feature set for the target user, wherein the at least one feature set for the target user is determined based on at least one target message of the target user; and determine the gender identifier of the target user according to the target feature vector and the gender classification model. 16. The device of claim 15, wherein the processor is further configured to perform at least one of: acquiring at least one target message of the target user in each preset period of time, and determining the at least one feature set for the target user from the at least one target message; or acquiring at least one target message of the target user upon detection that target messages of the target user increases by a preset threshold number, and determining the at least one feature set for the target user from the at least one target message. 17. The device of claim 11, wherein the at least one feature set comprises at least one of a salutation feature set, an operation feature set, or an application feature set. 18. The device of claim 17, wherein the salutation feature set comprises a male salutation feature set and a female salutation feature set. 19. The device of claim 17, wherein the operation feature set comprises at least one of a number of times of online shopping, a number of times participating in group-shopping, or an amount of consumption per month. 20. The device of claim 17, wherein the application feature set comprises one of a number of application APP registration or gender-specific APP. 21. A non-transitory computer-readable storage medium storing instructions that, when executed by a processor of a device, cause the device to perform a method for conducting classification model training, the method comprising: acquiring sample feature vectors of a plurality of users according to at least one feature set for each of the users, wherein the at least one feature set for a user is determined based on at least one sample message of the user; determining gender identifiers of the users according to the sample feature vectors; and conducting training based on the sample feature vectors and the gender identifiers corresponding to the sample feature vectors to construct a gender classification model.