This thesis focuses on the accurate identification of multimode radar models using Support Vector Machines (SVM). Through an in-depth exploration of key aspects such as SVM model construction, optimization, and performance evaluation, it aims to solve the problem of precise classification of multimode radar models in complex electromagnetic environments and improve the efficiency of radar countermeasures and spectrum management. Research shows that the optimized SVM model can achieve an identification accuracy of over 90% for multimode radar models, providing reliable technical support for related fields.