Bone and joint diseases have been one of the most prevalent diseases in human history. With the acceleration of aging, these diseases have become increasingly widespread, posing great challenges to orthopedic surgeons. Research on image segmentation for human joints can assist doctors in clinical diagnosis and treatment. However, due to the presence of noise, blurring, and low contrast, medical image feature extraction is more difficult than ordinary images. In addition, most segmentation models only use simple skip connections between the encoder and decoder, without addressing the issues of information gaps and losses during the skip connection process. To this end, this study proposes the DH-Swin Unet algorithm for medical image segmentation. On the basis of the Swin-Unet model, the densely connected Swin Transformer block and hybrid attention mechanism are introduced into skip connection to enhance the feature information transmission. The real clinical data provided by a hospital ranking top three are used to evaluate the performance of the proposed method. The results show that the DSC and HD of the model reach 86.79% and 32.05 mm respectively, and the model has certain practical value in the clinical diagnosis of joint diseases.