A method for sample labeling, comprising: acquiring a plurality of historical loss assessment images; determining, on the basis of saliency detection, a plurality of candidate damage areas from the plurality of historical loss assessment images, and using images of the plurality of candidate damage areas as a plurality of candidate samples; clustering the plurality of candidate samples into a plurality of class clusters, wherein clustering the plurality of candidate samples into a plurality of class clusters comprises: determining, on the basis of an image feature extraction model, a feature vector corresponding to each of the plurality of candidate samples to obtain a plurality of feature vectors; and clustering the plurality of feature vectors to obtain the plurality of class clusters; determining a plurality of center candidate sample sets respectively corresponding to class cluster centers of the plurality of class clusters, each of the class cluster centers being located at a center point in a vector space mapped by the feature vector corresponding to each candidate sample in the class cluster; receiving a manual labeling result, the manual labeling result being obtained by performing damage classification labeling on at least a portion of the plurality of center candidate sample sets, wherein respective center candidate samples in the same center candidate sample set are labeled with the same damage classification; and performing, according to the manual labeling result, damage classification labeling on other candidate samples in the class clusters to which the at least a portion of center candidate sample sets belong to obtain a plurality of labeled samples labeled with damage classifications, wherein the other candidate samples are labeled with the same damage classification as the center candidate sample set. acquiring a plurality of historical loss assessment images; determining, on the basis of saliency detection, a plurality of candidate damage areas from the plurality of historical loss assessment images, and using images of the plurality of candidate damage areas as a plurality of candidate samples; clustering the plurality of candidate samples into a plurality of class clusters, wherein clustering the plurality of candidate samples into a plurality of class clusters comprises: determining, on the basis of an image feature extraction model, a feature vector corresponding to each of the plurality of candidate samples to obtain a plurality of feature vectors; and clustering the plurality of feature vectors to obtain the plurality of class clusters; determining, on the basis of an image feature extraction model, a feature vector corresponding to each of the plurality of candidate samples to obtain a plurality of feature vectors; and clustering the plurality of feature vectors to obtain the plurality of class clusters; determining a plurality of center candidate sample sets respectively corresponding to class cluster centers of the plurality of class clusters, each of the class cluster centers being located at a center point in a vector space mapped by the feature vector corresponding to each candidate sample in the class cluster; receiving a manual labeling result, the manual labeling result being obtained by performing damage classification labeling on at least a portion of the plurality of center candidate sample sets, wherein respective center candidate samples in the same center candidate sample set are labeled with the same damage classification; and performing, according to the manual labeling result, damage classification labeling on other candidate samples in the class clusters to which the at least a portion of center candidate sample sets belong to obtain a plurality of labeled samples labeled with damage classifications, wherein the other candidate samples are labeled with the same damage classification as the center candidate sample set. <EOS>