Knee osteoarthritis is often misdiagnosed due to unclear categorization during examination, which affects the therapeutic effect, and existing detection methods perform poorly in terms of detection accuracy and accurate differentiation of lesion categories. To address the above problems this paper proposes an improved classification algorithm based on YOLOv8 for the detection of osteoarthritis of the knee joint, aiming to improve the accuracy of detection and classification. First, the distribution mapping module (V-PENet) is designed to help YOLO network to be trained better by preprocessing the input images and mapping the complex data distributions, uniformly, into simple data distributions. Meanwhile, a semantic perception module (PTB) is added to enhance the model's context-awareness ability and its understanding of global information. Secondly, the CA coordinate attention mechanism is introduced to enrich the feature information and further enhance the model's ability to capture mesoscale target information. Finally, the WIoUv3 loss function is used to replace the original CIoU loss function to optimize the positioning accuracy. Compared with the benchmark model YOLOv8n, the accuracy is improved by 9.9%, mAP@0.5 reaches 81.2% and mAP0.5:0.95 reaches 64.3%. Compared with other detection methods, the improved model proposed in this paper has a significant advantage in accuracy and can better meet the needs of knee osteoarthritis detection and classification.