With the advancement of artificial intelligence, the home care environment for elderly users is becoming increasingly intelligent and systematic. The context aware human–computer interaction technology of sustainable aging-friendly smart homes can effectively identify user needs, enhance energy efficiency, and optimize resource utilization, thereby improving the convenience and sustainability of smart home care services. This paper reviews literature and analyzes cases to summarize the background and current state of context-aware interaction experience research in aging-friendly smart homes. Targeting solitary elderly users aged 60–74, the study involves field observations and user interviews to analyze their characteristics and needs, and to summarize the interaction design principles for aging-friendly smart homes. We explore processes for context-aware and methods for identifying user behaviors, emphasizing the integration of green, eco-friendly, and energy-saving principles in the design process. Focusing on the living experience and quality of life for elderly users living alone, this paper constructs a context-aware user experience model based on multimodal interaction technology. Using elderly falls as a case example, we design typical scenarios for aging-friendly smart homes from the perspectives of equipment layout and innovative hardware and software design. The goal is to optimize the home care experience for elderly users, providing theoretical and practical guidance for smart home services in an aging society. Ultimately, the study aims to develop safer, more convenient, and sustainable home care solutions.

This study introduces a design transformation model for AI-generated Chinese traditional architectural images (SD Lora&Canny) based on Stable Diffusion (SD). By integrating parameterization techniques such as Low-Rank Adaptation (Lora) and edge detection algorithms (Canny), the model achieves precise restoration of the architectural form, color elements, and decorative symbols in Chinese traditional architecture. Using the Beijing Drum Tower as the experimental subject, statistical analysis software (SPSS V28.0) was employed to conduct a quantitative evaluation and comparative analysis of architectural images generated by the DALL-E, MidJourney, SD, and SD Lora&Canny models. The results demonstrate that the SD Lora&Canny model significantly outperforms traditional generation tools in restoration accuracy and visual fidelity. Finally, this study applied the SD Lora&Canny model to create the digital cultural product AR Drum and Bell Tower Fridge Magnet, showcasing its practical application in digital cultural creation and verifying its innovative potential in the digital preservation and transmission of Chinese traditional architecture.

Artificial Intelligence Generated Content (AIGC) technology demonstrates significant potential in the fields of cultural heritage digitalization and cultural tourism design. However, when confronted with specific subjects such as Chinese traditional architecture, which embodies rich cultural connotations and complex visual elements, existing technologies still exhibit limitations in understanding vague user requirements, ensuring cultural accuracy, and generating diverse and high-fidelity images. To address these challenges, this study proposes a text-to-image generation framework oriented towards Chinese traditional architecture, based on multi-agent collaboration. This framework integrates multiple intelligent agents responsible for user intent understanding, creative prompt generation, traditional architectural image generation, aesthetic and cultural relevance assessment, and collaborative workflow scheduling. By constructing a Chinese Traditional Architecture Cultural Knowledge Base (exemplified by the Beijing Central Axis) and designing a collaborative workflow among the agents, the framework can efficiently and accurately transform users’ colloquial and vague descriptions into Chinese traditional architectural images with profound cultural connotations and high visual fidelity. Experimental results demonstrate that this collaborative multi-agent framework significantly outperforms baseline models in terms of handling vague inputs, ensuring the cultural accuracy of generated images, enhancing user intent matching, and creative diversity. This research not only provides new theoretical perspectives and practical pathways for AI technology in the digital preservation, creative transformation, and intelligent design of cultural heritage, but also offers an effective tool for empowering innovative digital cultural tourism experiences, holding significance for technological exploration and cultural inheritance.