Data-driven artificial intelligence (AI)-based weather prediction (AIWP) models have demonstrated significant potential in weather forecasts, facilitating paradigm shift of prediction from a deductive to an inductive inference. However, this shift raises concerns regarding the performance of the AIWP models in severe weather forecasting. Tropical cyclones (TCs) are one of the most typical cases of severe weather forecasting. In this study, we compare forecasts of Western Pacific TCs in 2023 produced by the AIWP model, Pangu-Weather, with those generated by numerical weather prediction (NWP) models, specifically the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP), in the operational context. We analyze the impact of different initial conditions on the AIWP model Pangu-Weather, in TC forecasting. Our analysis includes statistical evaluation of forecast skills related to TC activity, track, intensity, and a case study on the physical structure of TCs. The Pangu-Weather model exhibits superior forecast skills compared to the NWP model regarding TC tracks and environmental variables within TC activity domains, particularly at longer leading times. However, the overly smooth forecasts from Pangu-Weather lead to significant underestimations of intensity and a weakened dynamic-thermodynamic structure of TCs. Additionally, Pangu-Weather shows reduced sensitivity to initial conditions concerning TC structure and intensity, potentially attributable to the limitations of the training dataset and deep learning model employed. Enhancing the application of higher-quality initial conditions and the exploring hybrid models that integrate physical processes with data-driven methods could significantly improve the effectiveness of AIWP models in severe weather forecasting.