Gansu painted pottery has the most complete spatial and temporal sequence among all kinds of painted pottery cultures in China. However, no study has been specifically designed for the style transfer of Gansu painted pottery. To promote the excellent traditional Chinese culture, this research constructs the Gansu painted pottery dataset and proposes a geometric style transfer method. The method generates a neural distortion field that deforms Gansu painted pottery into the geometric style of the target object while maintaining the texture of the pottery. Two modules are incorporated into the network structure, namely position embedding and feature enhancement, to improve the quality of feature encoding. Shape consistency loss and a smooth regularization term are introduced to the loss function to prevent the details of the painted pottery from mutating and improve the deformation effect. The experimental results show that the model can achieve large-scale geometric style transfer between Gansu painted pottery and objects from different classes, maintaining the details of the pottery and providing new visual experiences.