1. A method for labeling training samples, comprising: inputting M unlabeled first training samples into a first classifier to obtain a first forecasting result of each first training sample in the M first training samples, M being an integer greater than or equal to 1; selecting N first training samples as second training samples from the M first training samples based upon the first forecasting result of each first training sample, N being an integer greater than or equal to 1 and less than or equal to M; inputting the N second training samples into a second classifier to obtain a second forecasting result of each second training sample in the N second training samples, the first classifier and the second classifier being independent of each other; selecting P second training samples from the N second training samples based upon the second forecasting result of each second training sample, P being an integer greater than or equal to 1 and less than or equal to N; selecting Q first training samples from other first training samples based upon the first forecasting results of other first training samples in the M first training samples apart from the N second training samples and the value of P, Q being an integer greater than or equal to 1 and less than or equal to a difference between M and N; and generating P labeled second training samples based upon second forecasting results of the P second training samples and each of the second training samples therein; and generating Q labeled first training samples based upon first forecasting results of the Q first training samples and each of the first training samples therein.