Wheels are an essential part of railway trains; thus, defects on the wheel tread present serious risk regarding the safety of railway trains. Due to the limited samples of wheel tread defects in practice, the corresponding supervised detection model is insufficient. To solve this problem, an unsupervised knowledge distillation anomaly detection model is proposed to detect wheel tread anomalies. Accordingly, UNet is employed to segment the tread region and reduce the influence of non-tread regions on the anomaly detection model. An attention mechanism is then added after the multiscale feature fusion to improve the ability of the student network to reconstruct normal features in the reverse knowledge distillation structure, as well as enhance the reconstruction of normal features. From the experimental results, the improved model achieves the performance indexes of 93.8% area under receiver operating characteristic curve, 82.3% precision, 95.4% recall, and 87.0% accuracy considering the railway wheel tread dataset. Compared with the original model, the detection performance of the model is improved.