1. A classifier training method, comprising: acquiring 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 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 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 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 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.