Aiming at the problems of low efficiency, high cost, insufficient automatic detection label samples and high missed detection rate in the surface defect detection of ceramic tiles, a selfsupervised learning model is proposed, without a large number of defect samples, the detection and location of common defects on the surface of ceramic tiles can be realized. Selfsupervised learning generates negative samples through sample expansion, and uses distributionaugmented contrastive learning to improve data irregularity and expand sample distribution, thereby reducing the consistency of comparative representation and making the representation feature distribution consistent with the classification target. Based on selfsupervised learning representation, a class of classifiers is constructed to achieve accurate anomaly detection and localization. The experimental results show that compared with the other two advanced methods, under the standard evaluation criterion(AUROC) of anomaly detection, the anomaly detection rate is increased by 371% and 274% respectively; the abnormal location rate increased by 122% and 401% respectively, with more reliable detection performance.