Urban historical and cultural districts, serving as multi-functional compounds integrating cultural preservation, consumer experience, and economic growth, are increasingly becoming the preferred choice for in-depth tourism under the trend of historical heritage protection and consumption upgrading. Due to the complexity of the construction purpose, inherent functions, and operational management of historical districts, scientifically and rationally evaluating them poses a challenge. This paper attempts to construct an evaluation method for the tourism competitiveness of urban historical and cultural districts based on multi-source data and the Analytic Hierarchy Process (AHP) method. First, based on the model of destination competitiveness and combined with literature research and open-ended expert interviews, an evaluation framework for the tourism competitiveness of urban historical and cultural districts is established, using the AHP method to calculate the specific weights of each evaluation indicator. Then, the corresponding data sources for each indicator and the data processing and calculation methods are further clarified. To verify the effectiveness of the proposed evaluation model, this paper selects three key historical and cultural districts in Suzhou City, calculates the tourism competitiveness of each district based on the proposed model, and collects tourist satisfaction surveys from the three districts for cross-validation with the evaluation results. The experimental results show that the evaluation model is reliably effective in assessing the cultural, commercial, and tourism service aspects of historical districts, thereby providing a theoretical basis for future tourism decision-making information systems and practical applications of historical districts.

With the development of Internet technology and the digital market in China, consumption through e-commerce platforms and giving online reviews after purchase have become mainstream. However, current market research still uses traditional methods of surveys and questionnaires, which largely influences the efficiency of relevant enterprises in finding key product issues and lowers new product performance. This paper conducts the aspect-based sentiment analysis (ABSA) method to study the impact of consumer sentiment involvement (CSI) on new product performance. We took the ceramic industry as a case study, and collected 3.22 million consumer responses for ABSA. A total of 22 performances of new products were analyzed for CSI hypothesis testing. We found that CSI big data analytics were positively related to new product performance and enterprise innovation. Our study contributes in three ways. First, we extend the concept of co-creation in open innovation studies into an intelligent data intensive context. The data-driven open innovation is conducive to product design and new product performance. Second, we enrich the empirical industry of ABSA sentiment analysis in the Chinese ceramic industry. Third, we contribute to the collaboration method to motivate consumers to participate, which means to make effective product reviews in this research.

Rural public cultural spaces serve as vital venues for information exchange, interpersonal interaction, and cultural and leisure activities in rural communities. Since the Rural Revitalization Strategy was proposed in 2017, the planning and provision of rural public cultural spaces have attracted increasing attention in China. However, many such spaces remain underutilized, accompanied by low levels of user satisfaction among villagers. A key reason for this is the mismatch between standardized spatial configurations and villagers’ dynamic functional needs. Drawing on Hertzberger’s theory of spatial polyvalence, this study proposes a framework to evaluate spatial flexibility in rural public cultural spaces. The framework introduces quantitative indicators and computational methods across two dimensions: “competence”, referring to a space’s potential to accommodate multiple functions, and “performance”, reflecting the efficiency of functional transformation during actual use. Employing the proposed method, this study conducts a case analysis of the Xiangyang Village Neighborhood Center in Shanghai to evaluate its spatial characteristics and actual usage. The evaluation reveals two key issues at the overall level: (1) many residual spaces remain undesigned and lack strategies to support spontaneous use; (2) the spatial layout shows rigid public–private divisions, with little adaptability. At the room level, spaces such as the elevator, hairdressing room, party secretary’s office, and health center are functionally rigid and underutilized. Drawing on exemplary cases, this study proposes several key strategies such as (1) optimizing and innovatively activating residual spaces, (2) integrating multifunctional programs, and (3) improving spatial inclusiveness.

Empowered by the ubiquitous sensing capabilities of Internet of Things (IoT) technologies, smart communities could benefit our daily life in many aspects. Various smart community studies and practices have been conducted, especially in China thanks to the government’s support. However, most intelligent systems are designed and built individually by different manufacturers in diverging platforms with different functionalities. Therefore, multiple individual systems must be deployed in a smart community to have a set of functions, which could lead to hardware waste, high energy consumption and high deployment cost. More importantly, current smart community systems mainly focus on the technologies involved, while the effects of human activity are neglected. In this paper, a fourth-order tensor model representing object, time, location and human activity is proposed for human-centered smart communities, based on which a unified smart community system is designed. Thanks to the powerful data management abilities of a high-order tensor, multiple functions can be integrated into our system. In addition, since the tensor model embeds human activity information, complex functions could be implemented by exploring the effects of human activity. Two exemplary applications are presented to demonstrate the flexibility of the proposed unified fourth-order tensor-based smart community system.

Recent advancements in artificial intelligence, particularly in generative technologies, have significantly redefined the design paradigm for community public cultural spaces, shifting from a traditionally designer-centric model to one that emphasizes multi-stakeholder co-creation. This paper focuses on the design of public cultural spaces at the community scale, proposing a generative dynamic workflow-based co-creation framework that integrates large language models (LLMs) with text-to-image technologies. The framework includes a natural dialogue-based needs-capturing module, a needs analysis module, and a needs expression text-to-image module. This study validates the proposed framework by developing a system prototype for renovating a public space in a student dormitory at Tongji University’s Jiading campus. The results show that the prototype demonstrates good usability and a relatively satisfactory capability in capturing user requirements. These findings indicate that this research helps address key limitations in traditional community design practices, such as limited resident participation, inefficient integration of diverse needs, and slow iteration processes.