Osteoarthritis is the most common joint disease in middle-aged and elderly people. The disease and its complications account for 10 per cent of global medical problems. Among them, Osteoarthritis of the knee is the most serious and the risk of disability is very high. Early detection and interventional treatment is of great significance to relieve the symptoms and reduce the harm. In this paper, a large number of knee joint DR image data were collected. Various texture features and fusion features were extracted from the obtained data. Then, various combinations of extracted feature vectors were used as input to the training support vector machine model. We use the grid search method to optimize the parameters. The highest accuracy of the trained model on the test set can reach 84.29%, which has good intelligent classification and diagnosis performance. Using the trained SVM model can effectively grade knee osteoarthritis and assist doctors in diagnosis, which is of great significance for early diagnosis and early intervention treatment of knee osteoarthritis.