Due to the strong visual impact and color contrast inherent in woodcut style design, it has been applied in animation and comics. However, traditional woodcuts, hand-drawn, and previous computer-aided methods have yet to address the issues of dwindling design inspiration, lengthy production times, and complex adjustment procedures. We propose a novel network framework, the Woodcut-style Design Assistant Network (WDANet), to tackle these challenges. Notably, our research is the first to utilize diffusion models to streamline the woodcut-style design process. We curate the Woodcut-62 dataset, which features works from 62 renowned historical artists, to train WDANet in absorbing and learning the aesthetic nuances of woodcut prints, offering users a wealth of design references. Based on a denoising network, our WDANet effectively integrates text and woodcut-style image features. WDANet allows users to input or slightly modify a text description to quickly generate accurate, high-quality woodcut-style designs, saving time and offering flexibility. As confirmed by user studies, quantitative and qualitative analyses show that WDANet outperforms the current state-of-the-art in generating woodcut-style images and proves its value as a design aid.