A detection algorithm combining knowledge distillation and attention mechanism is proposed to solve the problem that multi-scale target of the hot spot fault of photovoltaic panel in a complex environment leads to difficult detection. To efficiently extract and retain fault feature information， a module that integrates higher-order spatial interaction and channel attention was designed to improve the expression ability of fault feature information. To further enhance the ability of expressing target information in a complex background， an attention module combining channel and location information was constructed to improve the recognition accuracy of fault location information. The parameters of teacher network were transferred to student network by knowledge distillation， and the detection accuracy of student network was improved without adding any complexity. A focal-CIoU loss function was introduced to accelerate network convergence and improve detection performance. In verifying the effectiveness of the proposed algorithm against eight classical algorithms， the experimental results show that the proposed algorithm has the highest detection accuracy （84.8%）， and the detection speed can reach 142 FPS for images with a resolution of 640×512.