The traditional surface defect detection methods can only detect obvious defect contours with high contrast or low noise. In order to solve the problem， a surface defect detection method based on auto-encoding and knowledge distillation was proposed to accurately locate and classify the defects that appeared in the input images captured from the actual industrial environment. Firstly， a new Cascaded Auto-Encoder （CAE） architecture was designed to segment and locate defects， whose purpose was to convert the input original image into the CAE-based prediction mask. Secondly， the threshold module was used to binarize the prediction results， thereby obtaining the accurate defect contour. Then， the defect area extracted and cropped by the defect area detector was regarded as the input of the next module. Finally， the defect areas of the CAE segmentation results were classified by knowledge distillation. Experimental results show that， compared with other surface defect detection methods， the proposed method has the best comprehensive performance， and its average accuracy of defect detection is 97.00%. The proposed method can effectively segment the smaller defects with blurred edges， and meet the engineering requirements for real-time segmentation and detection of item surface defects.