Heng Wang

In the context of immersive technologies deeply integrated into the cultural tourism industry, immersive cultural tourism has become an important means of heritage revitalization. Immersive experience is both a crucial consumer element and a key indicator for evaluating the attractiveness of cultural tourism landscapes. This study evaluates the post-use experience of the cultural tourism landscape of Xidajie Street in Baoding from the perspective of tourist immersion. Through a literature review, investigation of typical immersive districts, and expert interviews, we extract immersive cultural tourism landscape evaluation criteria based on a depth model of immersion, focusing on three dimensions: narrative, enclosure, and interaction. Subjective perception data from tourists is then collected through a survey, and IPA (Importance–Performance Analysis) is employed to identify the strengths and weaknesses of Xidajie’s cultural tourism landscape. The results show that Xidajie excels in spatial environment shaping and historical preservation, but has room for improvement in cultural narrative extension, contextual immersion, and interactive experiences. Therefore, strategies are proposed to enhance the cultural IP, establish a complete narrative structure, create authentic enveloping environments, and enrich interactive games to build a high-quality online and offline immersive cultural tourism landscape. This aims to promote the renewal of Xidajie and the dynamic transmission of Baoding’s local culture.

The increasing volume of motorized traffic not only negatively impacts the structural preservation and overall planning of individual buildings within the block but also disrupts the originally harmonious and pleasant spatial environment of the area. Walking, as a primary mode of urban transportation, plays a crucial role in preserving the unique characteristics of historical blocks, enhancing the quality of the urban environment, and achieving long-term sustainable urban development. This study takes the Five Avenues historical block as a case, assessing the current walking environment from the perspective of Post-Occupancy Evaluation (POE). Machine learning techniques (including web scraping, the TF-IDF algorithm, and the LDA model) were employed to collect and analyze user feedback data, assisting in constructing walking environment satisfaction indicators. A total of 19 key factors affecting walking satisfaction were identified. Paired sample t-tests, ANOVA, and reliability and validity analyses were applied to examine the feasibility and practicality of the questionnaire content. Finally, using Importance–Performance Analysis (IPA), the improvement priorities for walking environment indicators were clearly defined. Although the overall satisfaction index of the Five Avenues is comparatively high, unobstructed pathways have the greatest impact on walking environment satisfaction, followed by the rationality of guiding signage facilities, and then by public security management and facility maintenance. Furthermore, visitors prioritize factors such as the cultural recognizability of the area, travel convenience, green space accessibility, and the sidewalk width proportion; they are less focused on the functional aspects of the walkways. Based on the analysis results from POE and machine learning, targeted strategies for improving the walking environment in historical blocks were proposed, aiming to provide a more comprehensive basis for improving the walking environments of similar blocks.