Although functional identifiability represents a key aspect for promoting visual connotation and sustainable usability in historic building groups, there is still no consensus on how to quantitatively describe its identification basis at a large scale. The recent emergence of the potentiality of deep learning and computer vision has provided an alternative to traditional empirical-based judgment, which is limited by its subjective bias and high traversal costs. To address these challenges, this study aims to build a workflow for a visual analysis of function and facade to extract the different contributions that facade elements provide to functional expression. The approach is demonstrated with an experiment on a section of the Chinese Eastern Railway (CER) where large-scale historical buildings images were categorized to identify functions using deep learning, together with activation and substance for visual calculations. First, the dataset aggregated with images of historic buildings along the CER was used to identify functional categories using SE-DenseNet merging channel attention. The results of the model visualized using t-SNE and Grad-CAM were then used to analyze the relationships of facade features across functional categories and differences in elemental feature representation across functional prototypes. The results show the following: (1) SE-Densenet can more efficiently identify building functions from the closely linked facade images of historic building groups, with the average accuracy reaching 85.84%. (2) Urban–rural differences exist not only in the count of spatial distributions among the CER’s historic building groups, but also in a significant visual divergence between functions related to urban life and those involved in the military, industry, and railways. (3) Windows and walls occupy areas with more characteristics, but their decorative elements have a higher intensity of features. The findings could enhance the objective understanding and deeper characteristics of the historical building group system, contributing to integrated conservation and characteristic sustainability.

Against the backdrop of rapid development in the IST industry, addressing issues such as regional homogeneity and uneven spatiotemporal development requires scientific identification and analysis of related resources to support sustainable regional IST development and promote high-quality regional economic growth. This study proposes a framework based on “policy orientation–theoretical support–regional adaptation,” utilizing machine learning to construct a multi-dimensional evaluation index system for IST development potential. By combining subjective and objective criteria to determine indicator weights, a scientific evaluation system is established, with visual analysis conducted through Geographic Information System (GIS). The research selects 22 indicator factors across four dimensions: natural environmental suitability, socio-economic support capacity, regional transportation accessibility, and tourism appeal. Through weighted superposition analysis, it achieves visual representation of spatial differentiation characteristics in the development potential levels of IST in Heilongjiang Province. Results demonstrate a distinct “V”-shaped distribution of high development potential, primarily concentrated in the Greater Khingan Range region, Harbin–Mudanjiang border zone, and Jiamusi, with gradual decline from the core “V”-shaped area to both sides. The proposed evaluation index system provides scientific quantitative decision-making support for regional IST planning, and this methodology also holds reference value for evaluating other tourism industry developments.