The article presents a perspective on online museum design by conducting a systematic review. The study aims to synthesize key findings related to online museums, focus on issues related to design as well as identify priority areas for further research in the design of online museums. The systematic review process followed the guidelines outlined in the PRISMA 2020 recommendations to select relevant articles. A search was conducted in the Web of Science database with the specific topic of “online museum design.” A total of 148 publications published between 2019 and 2023 were identified and selected for the review. A keywords map was generated based on the bibliographic data of the selected articles. By conducting a systematic review of publications from the Web of Science database, the study identifies five clusters of research in online museum design. These clusters cover topics such as visitor behavior, accessibility, authenticity, immersive technologies, and technology adoption. The findings highlight key aspects of design that impact user engagement, cultural heritage, virtual reality, and the intersection of technology and tourism. The review also includes a case study of online museums representing various types of artifacts, including historical and cultural heritage, art, science, nature, clothing, and museums for children. Overall, the systematic review provides valuable insights for researchers, practicing designers, and museum professionals involved in creating and curating online museum experiences. Further research involves a review of publications using other scientific databases, as well as using the term “virtual museum” for a wider coverage of publications in this direction.

Traditional Chinese patterns, as one of the core cultural heritage carriers, face inheritance challenges due to traditional design’s heavy reliance on professional expertise and lengthy cycles. The rise of Artificial Intelligence Generated Content (AIGC) has introduced a human-intelligence collaborative paradigm to address these issues—this article focuses on leveraging AIGC for intelligent design of traditional Chinese patterns. Specifically, a dataset of traditional Chinese patterns was first constructed, featuring 30,000 images represented by Tuanke patterns. Subsequently, ChiPa, an intelligent design method tailored to traditional Chinese patterns, was proposed. This method enables simultaneous control over both the content and color. ChiPa’s core is a novel Structure Projector (Feature Map Mixture + Latent Space Mapper with MCConv), which maps sketches to StyleGAN’s latent space to generate structurally accurate grayscale Tuanke patterns. For color customization, ChiPa employs an arbitrary style transfer module to render grayscale patterns via user-specified references. Extensive experiments validate ChiPa’s superiority: it outperforms SOTA methods in all metrics; 3 domain experts and 30 volunteers rated it highest in structural standardization, stylistic consistency, and symbolic accuracy. Case studies demonstrate applications in cultural (e.g., packaging) and commercial (e.g., sofa upholstery) products. ChiPa lowers public participation barriers, expands Tuanke patterns’ carriers, and promotes living conservation and dissemination of Chinese traditional cultural heritage.