A method for training an image recognition model, applied to a terminal device, the method comprising: training an image recognition model by using a tagged image sample set; recognizing multiple to-be-tagged image samples by using the image recognition model to obtain a confidence level of a recognition result corresponding to each of the multiple to-be-tagged image samples; selecting, in the multiple to-be-tagged image samples, at least one to-be-tagged image sample of which the confidence level is within a preset interval to form a first to-be-tagged image sample set, wherein the preset interval is from 0.5 to 0.8; acquiring manually tagged tags of to-be-tagged image samples in the first to-be-tagged image sample set; updating the tagged image sample set by adding the to-be-tagged image samples with the tags to the tagged image sample set, to obtain an updated tagged image sample set; and training the image recognition model by using the updated tagged image sample set, wherein acquiring the manually tagged tags of the to-be-tagged image samples in the first to-be-tagged image sample set comprises: determining information entropy of a to-be-tagged image sample according to image features of the to-be-tagged image sample in the first to-be-tagged image sample set; characterized by clustering to-be-tagged image samples of which the information entropy meets a predetermined condition to obtain K clusters and a cluster center of at least one cluster, K being a positive integer, wherein clustering the to-be-tagged image samples of which the information entropy meets the predetermined condition comprises clustering the to-be-tagged image samples of which the information entropy is greater than an entropy threshold value or clustering a predetermined number of to-be-tagged image samples with maximum information entropies; selecting, from the at least one cluster, one or more to-be-tagged image samples closest to a cluster center of the cluster as target to-be-tagged image samples having cluster representativeness, to form a second to-be-tagged image sample set; and acquiring manually tagged tags of the target to-be-tagged image samples in the second to-be-tagged image sample set. training an image recognition model by using a tagged image sample set; recognizing multiple to-be-tagged image samples by using the image recognition model to obtain a confidence level of a recognition result corresponding to each of the multiple to-be-tagged image samples; selecting, in the multiple to-be-tagged image samples, at least one to-be-tagged image sample of which the confidence level is within a preset interval to form a first to-be-tagged image sample set, wherein the preset interval is from 0.5 to 0.8; acquiring manually tagged tags of to-be-tagged image samples in the first to-be-tagged image sample set; updating the tagged image sample set by adding the to-be-tagged image samples with the tags to the tagged image sample set, to obtain an updated tagged image sample set; and training the image recognition model by using the updated tagged image sample set, wherein acquiring the manually tagged tags of the to-be-tagged image samples in the first to-be-tagged image sample set comprises: determining information entropy of a to-be-tagged image sample according to image features of the to-be-tagged image sample in the first to-be-tagged image sample set; characterized by clustering to-be-tagged image samples of which the information entropy meets a predetermined condition to obtain K clusters and a cluster center of at least one cluster, K being a positive integer, wherein clustering the to-be-tagged image samples of which the information entropy meets the predetermined condition comprises clustering the to-be-tagged image samples of which the information entropy is greater than an entropy threshold value or clustering a predetermined number of to-be-tagged image samples with maximum information entropies; selecting, from the at least one cluster, one or more to-be-tagged image samples closest to a cluster center of the cluster as target to-be-tagged image samples having cluster representativeness, to form a second to-be-tagged image sample set; and acquiring manually tagged tags of the target to-be-tagged image samples in the second to-be-tagged image sample set. determining information entropy of a to-be-tagged image sample according to image features of the to-be-tagged image sample in the first to-be-tagged image sample set; characterized by clustering to-be-tagged image samples of which the information entropy meets a predetermined condition to obtain K clusters and a cluster center of at least one cluster, K being a positive integer, wherein clustering the to-be-tagged image samples of which the information entropy meets the predetermined condition comprises clustering the to-be-tagged image samples of which the information entropy is greater than an entropy threshold value or clustering a predetermined number of to-be-tagged image samples with maximum information entropies; selecting, from the at least one cluster, one or more to-be-tagged image samples closest to a cluster center of the cluster as target to-be-tagged image samples having cluster representativeness, to form a second to-be-tagged image sample set; and acquiring manually tagged tags of the target to-be-tagged image samples in the second to-be-tagged image sample set.