1. A classifier training method, comprising: acquiring, by a processor of an identity authentication server, a training sample set, each training sample in the training sample set comprising a user identity attribute and a feature value corresponding to a preset classification condition feature; determining, by the processor of the identity authentication server, a classification condition at a root node according to one preset classification condition feature, performing classification on the training samples in the training sample set according to the classification condition at the root node, and acquiring a classification subset corresponding to a child node of the root node; and using the child node of the root node as a current node; determining, by the processor of the identity authentication server, a classification condition at the current node according to another preset classification condition feature, performing classification on training samples in a classification subset corresponding to the current node according to the classification condition at the current node, and acquiring a classification subset of a child node of the current node; using, by the processor of the identity authentication server, the child node of the current node as a current node, continuously performing the step of determining a classification condition at the current node according to another preset classification condition feature, performing classification on training samples in a classification subset corresponding to the current node according to the classification condition at the current node, and acquiring a classification subset of a child node of the current node, until feature values corresponding to the same preset classification condition feature of training samples in the classification subset corresponding to the current node are respectively the same, or a layer number of the current node reaches a designated layer number; and determining, by the processor of the identity authentication server, a user identity classification result at the current node according to a user identity attribute corresponding to the maximum number of training samples in the classification subset corresponding to the current node, and acquiring a decision tree classifier; wherein the determining a classification condition at a root node according to one preset classification condition feature comprises: counting the number of training samples, in the training sample set, in which a user identity attribute corresponding to each feature value of each preset classification condition feature is a legal user, and determining the classification condition at the root node according to corresponding preset classification condition features corresponding to feature values having the maximum counted number in the training sample set; and the determining a classification condition at the current node according to another preset classification condition feature comprises: determining residual preset classification condition features corresponding to the current node except for the preset classification condition feature used for determining the classification condition at a node of a current path, counting the number of training samples, in the classification subset corresponding to the current node, in which a user identity attribute corresponding to each feature value of each residual preset classification condition feature is a legal user, and determining the classification condition at the current node according to residual preset classification condition features corresponding to feature values having the maximum counted number corresponding to the current node.