This paper proposes a data-driven fuzzy logic control method to improve the Dynamic Window Approach (DWA) for path planning of Unmanned Surface Vehicles (USVs). The proposed method aims to reduce the subjectivity introduced by human factors in the parameter settings of conventional fuzzy logic control. A data-driven dataset was constructed by extracting parameters from conventional fuzzy logic control algorithms, and a fuzzy neural network was employed to derive a new fuzzy logic controller from this dataset. The resulting controller exhibits a more rational distribution of variables compared to conventional fuzzy logic controllers, demonstrating the superiority of controllers generated by neural networks. Numerical simulations show that proposed the data-driven USVs path planning method offers improvements in terms of path length, navigation time, and reduced turning angles.