Typhoon track prediction is extremely important for disaster prevention and mitigation in coastal cities. The traditional numerical weather forecast WRF model can provide relatively accurate short-term forecast, but it is difficult to deal with the nonlinearity and complexity of typhoon track. This research focuses on applying deep learning approaches to refine WRF typhoon path predictions, seeking to improve accuracy by addressing the limitations inherent in conventional models. By combining the physical basis of NWP model with the nonlinear fitting ability of deep learning algorithm, mainly focusing on the Long Short-Term Memory network and transformer model, the best prediction performance can be achieved. The results of model validation experiments show that the hybrid method of WRF model and deep learning model can significantly improve the typhoon path prediction, and the prediction model using transformer performs better.