Tire defect detection has important reference significance for tire grading, and it is particularly important to study the high performance tire anomaly detection method. Based on reinforcement learning algorithm, an automatic image classification algorithm based on abnormal loss value is proposed. This method firstly by a large number of positive samples input to reduce the loss value of data after gradient update, with a small amount of the loss of the abnormal sample input values form the obvious difference, introducing neural sampler, enlarge abnormal loss of contour difference between samples and the positive samples and provide training to sVAE batch, then put the training result as input of abnormal classifier, Finally, the classification and location of anomaly detection are completed. Through comparative study, it is found that the anomaly detection algorithm proposed in this paper is obviously superior to other traditional image anomaly detection methods in tire defect sample sets.