New buildings continue to be constructed in historic districts as urbanization accelerates. This leads to incongruities between historic and modern buildings, as well as degradation of the historic landscape. As a means of resolving these problems, this study uses the expert scoring method to solicit opinions from relevant experts through anonymous methods for architectural samples and establishes two groups of architectural images that have been screened for objective color attribute analysis. Comparing the architectural color codes on site was conducted using the Chinese Architectural Color Card based on the Munsell color system. Furthermore, questionnaires were used to measure the differences in visual comfort between different building types by asking the subjects to rate architectural colors when viewing architectural images. This study will also combine the advantages of measurement and analysis of objective objects with the study of human perceptual experience in order to enhance its practicality. In general, the buildings in the West Nanjing Road Historic Landscape District are painted in warm yellow, and the public is aware of its comforting color. Both historical and modern buildings in the historic district are mostly of medium and high brightness, and both use low-purity colors for the main body of their structures. Despite some differences between people of different genders, ages, and literacy levels, all of them agree on the main tone of warm yellow color in architectural colors.

The wealth of published data are valuable because, in addition to contributing to the advancement of scientific, technical, and policy knowledge, they can also provide critical information and guidance regarding published content, subject changes, and trends that demand greater attention. In the 21st century, digital technologies play an increasingly important role in “data capture”, “building management”, “virtual reconstruction”, and “building restoration”. The indispensable role of digital technology in addressing “data capture”, “building management”, “virtual reconstruction”, and “building restoration” has resulted in the publication of several high-quality publications. In this study, we retrieve textual data from Web of Science and mine the content of the documentary data using COOC, VOSviewer, CiteNetwork, and academic influence evaluation (AIE) software to gain insights into the prospects and opportunities for historic architecture and digitization research. The results indicate that greater progress has been made in research on the use of digital technologies for the conservation of historic buildings from 2019 to 2023, but cross-disciplinary, cross-institutional, and cross-border collaboration should be enhanced. The research frontiers identified indicate that photogrammetry, 3D modeling, point cloud, and deep learning will require sustained attention in the near future. Additionally, computational analyses of academic influence reveal that Italian institutions and authors have dominated research in this field in recent years. A new strategy and framework for data-driven bibliometric analysis involving historical architecture and digitization techniques are presented in this study. Based on general bibliometric methods, this study innovatively explores the scientific knowledge base and knowledge flow of highly cited articles, provides comprehensive evaluation indicators such as H-index, G-index, P-index, and Z-index for high-impact journals, institutions, and authors, and proposes a COOC-based idea to address the consistency of data sources among multiple software.