Knee osteoarthritis is a common traumatic and degenerative bone and joint disease that can induce various pathological changes due to injuries to various knee structures. Magnetic resonance imaging plays a crucial role in the clinical diagnosis of knee osteoarthritis. Currently, the use of deep learning models to extract depth features from knee joint images and achieve segmentation and lesion recognition of various knee joint structures has become a research hotspot in the field of auxiliary diagnosis of knee joint diseases. First, this study discussed the advantages and disadvantages of various imaging techniques for the knee joint, focusing on magnetic resonance multisequence imaging technology. Then, it highlighted current status of deep learning models used for diagnosing knee joint cartilage, meniscus, and other tissue structural lesions. Furthermore, it addressed the limitations of existing recognition models and introduced two model optimization technologies: knowledge distillation and federated learning. Finally, this study concluded by outlining future research directions.