<p>Extreme weather caused by typhoons poses a severe threat to human life safety and socio-economic development, making accurate prediction of typhoon paths crucial. However, existing prediction models struggle to effectively handle and integrate heterogeneous data, overlooking deep correlations between the data, which in turn affects the accuracy of the models. This paper proposes a Dual-Encoder Spatiotemporal Fusion Model for Typhoon Path Prediction (DESF-Typhoon), designed to effectively integrate data from different time scales and extract the underlying relationships between the data, thereby improving prediction accuracy. Specifically, we designed a dual-encoder module that effectively captures complex nonlinear data structures, accounting for the intricate influences of factors such as geography and environment on path, while integrating spatial features of typhoon at different time scales. Subsequently, we introduced a feature interaction module to explore the relationships between different features, which can adaptively learn the interaction weights between them, resulting in a richer feature representation. We evaluated the model using the dataset from the China Meteorological Administration (CMA) and compared its performance with traditional prediction methods and deep learning-based approaches. The results demonstrate that the model significantly improves accuracy and robustness, making innovative contributions in data fusion and spatiotemporal feature modeling.</p>