A computer-implemented 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 M-N first training samples based upon the first forecasting results of the M first training samples and the value of P, Q being an integer greater than or equal to 1 and less than or equal to M-N; 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; 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; and using the P labeled second training samples and the Q labeled first training samples to build a classifier; wherein, said selecting the N first training samples comprises: obtaining a first probability that said first training samples indicated by the first forecasting result are of a designated type; and selecting, from the M first training samples, the N first training samples of which the first probability satisfies a pre-set first training condition as the second training samples, and wherein the first training condition comprises a probability that the first training samples indicated by the first forecasting result are of a designated type is greater than or equal to a first threshold value and is less than or equal to a second threshold value. 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 M-N first training samples based upon the first forecasting results of the M first training samples and the value of P, Q being an integer greater than or equal to 1 and less than or equal to M-N; 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; 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; and using the P labeled second training samples and the Q labeled first training samples to build a classifier; wherein, said selecting the N first training samples comprises: obtaining a first probability that said first training samples indicated by the first forecasting result are of a designated type; and selecting, from the M first training samples, the N first training samples of which the first probability satisfies a pre-set first training condition as the second training samples, and wherein the first training condition comprises a probability that the first training samples indicated by the first forecasting result are of a designated type is greater than or equal to a first threshold value and is less than or equal to a second threshold value. obtaining a first probability that said first training samples indicated by the first forecasting result are of a designated type; and selecting, from the M first training samples, the N first training samples of which the first probability satisfies a pre-set first training condition as the second training samples, and wherein the first training condition comprises a probability that the first training samples indicated by the first forecasting result are of a designated type is greater than or equal to a first threshold value and is less than or equal to a second threshold value.