In the field of digital design, a recent hot topic is the study of the interaction between spatial environment design and human factors. Electroencephalogram (EEG) and eye tracking can be used as quantitative analysis methods for architectural space evaluation; however, conclusions from existing studies on improving the quality of spatial environments based on human factors tend to remain qualitative. In order to realise the quantitative optimisation design of spatial elements from human physiological data, this research used the digital space optimisation method and perceptual evaluation research. In this way, it established an optimisation method for built space elements in real-time using human psychological indicators. Firstly, this method used the specific indicators of the Meditation value and Attention value in the human EEG signal, taking the ThinkGear AM (TGAM) module as the optimisation objective, the architectural space colour and the window size as the optimisation object, and the multi-objective genetic algorithm as the optimisation tool. Secondly, this research combined virtual reality scenarios and parametric linkage models to realise this optimisation method to establish a tool platform and workflow. Thirdly, this study took the optimisation of a typical living space as an example and recruited 50 volunteers to participate in an optimisation experiment. The results indicated that with the iterative optimisation of the multi-objective genetic algorithm, the specific EEG index decreases significantly and the standard deviation of the in-dex fluctuates and decreases during the iterative process, which further indicates that the optimisation method established in this study with the specific EEG index as the optimisation objective is effective and feasible. In addition, this study laid the foundation for more EEG indicators and more complex spatial element opti-misation research in the future.

Human experience in an architectural space is defined as the state of mind that is reflected on their physiological, emotional, and cognitive statuses. Ergonomic data, as an objective manifestation of quantifiable signals generated by the human body during specific spatial perception processes, serves as a vital foundation for spatial evaluation and guidance for optimization. Electroencephalogram (EEG) signals, as quantifiable sensory indicators directly arising from the interaction between individuals and external stimuli, hold substantial potential as a data-driven force and as a means of optimization assessment in the study of generative design. Although existing research has effectively established a unidirectional relationship between EEG and spatial-environment assessment, there is still a notable deficiency in addressing the creation of a two-way, mutually informative feedback mechanism. This study investigates the applicability of EEG signals as a data-driven basis for generative design across universal methods. It delves into various scales and scenarios of digital design, from the microscopic to the macroscopic, encompassing planar and volumetric visual elements, the design of architectural spatial environmental characteristics, and urban space design grounded in human perceptual sightlines. The research examines the viability and appropriateness of an interactive generative design method based on form generation, predicated on human-factor physiological data exemplified by EEG signals. This paper initially conducts a methodological and tool-based examination of current research in ergonomics-driven design and the use of EEG for design assessment, thereby discussing the objective feasibility of employing EEG in interactive generative design. Subsequently, the study establishes an integrated data flow system encompassing multiple hardware and software components to form a comprehensive workflow. Following this setup, empirical studies based on this method are conducted at different scales of application, yielding corresponding form-generative outcomes. Finally, this paper substantiates the rationality and feasibility of this framework in multi environment design domains.

In recent years, the research on digital design and perceptual evaluation has gradually become a hot topic in the field of digital design. Based on digital space optimization theory and perceptual evaluation tools, this study attempts to establish an optimization method to optimize built space elements in real-time using human psychological indicators. This method takes the specific indicators of the Meditation value and Attention value in the human EEG signal analyzed by the TGAM module as the optimization objective, the architectural space color and the window size as the optimization object, and the multi-objective genetic algorithm as the optimization tool. To realize this optimization method, this research combines virtual reality scene and parametric linkage model to establish tool platform and workflow. Taking the optimization of typical residential space as an example by recruiting 50 volunteers to participate in the experiment, this study concludes that this method is effective and feasible through experiment and quantitative analysis of experimental results and lays the foundation for more EEG indicators and more complex spatial element optimization research in the future.

Public open spaces (POSs) are key assets in renovating traditional residential areas, and many studies examine how their commercial attributes shape spatial cognition and consumer behavior to encourage activity and boost local consumption. However, there is a lack of response to the emotional feedback needs of locals in the renovation of public open spaces in historic communities. To fill this gap, this study proposes a VR-based multimodal perception assessment framework integrating immersive virtual reality (VR) experiments, electroencephalography (EEG) measurements, and AI-based landscape image segmentation to investigate how built environmental elements in public open spaces influence the spatial preferences and psychological responses of local residents within Quanzhou historic communities. By integrating EEG data with Likert-scale preference evaluations, it investigates how varying characteristics of visible landscape elements within POSs influence residents’ psychological responses. The results reveal that locally distinctive vegetation and heritage architectural remains in historical district public open spaces (HDPOSs) are positively correlated with relaxation and negatively correlated with attention levels. It is worth noting that recently designed or renovated spaces with preserved old vegetation are more popular and cause higher levels of relaxation among locals compared to more contemporary built-up areas, highlighting their cultural and ecological importance in urban settings. This study advances the fields of urban planning and historical preservation by advocating for the integration of local preferences and historical elements into POS design to foster community well-being and ensure cultural continuity.

In response to the lack of objective evaluation criteria for interpreting the spatial scales of historical streets, as well as the problem of fragmented and complex textures, this research proposes an analysis method for assessing the spatial scaling effects using the Ping Ge type map. Drawing on the Ping Ge cartographic methods from the Qing Dynasty (1644–1911), it connects ancient measurement systems with objective evaluation criteria based on object relations and utilizes surveying maps from the Republic of China (1912–1949) to clarify temporal attributes of texture. The study employs a typical case study to demonstrate the feasibility of using the Ping Ge type map in interpreting historical streets. By creating the Ping Ge type map of the East‒West Street in Quanzhou Ancient City and utilizing form diagrams, it reveals patterns such as scale multiplications within respective plots, transformations in linear sequences of streets, and increases or decreases in plot series scales. The case analysis indicates that the Ping Ge type map effectively uncover the spatial scaling characteristics of historical street layouts in China and can transform them into design resources that preserve the inner order of cities, thereby promoting spatial scaling awareness in the planning and design of regional architectural clusters.

Generating heritage facade images with recognizable regional style is increasingly feasible with diffusion models, but prompt engineering or standard fine-tuning alone rarely encodes the construction logic, spatial hierarchy, and symbolic rules that make such heritage architecture intelligible. This gap is critical for Huizhou traditional dwellings (HzTD), a distinctive type of vernacular architecture in southern Anhui Province, China, whose facade forms depend on typological relations among courtyard layouts, timber-frame bay rhythms, materials and decorative motifs. To address this gap, this study proposes a Knowledge-Augmented Stable Diffusion Model (KA-SDM) for semantic representation and controlled facade generation of HzTD. The framework first organizes construction knowledge into a CRM-inspired property graph schema implemented in Neo4j, where posts, spaces, ratios, wall plans, elements, and details are represented as queryable entities and relations. Rule-based graph inference then derives facade composition attributes, opening configurations, and detail semantics, which are converted by a Prompt-Conditioned Semantic Controller (PCSC) into structured prompt fragments and ControlNet masks. Experiments on Huizhou dwelling facade images from Xidi Village compare KA-SDM with a Stable Diffusion (SD) baseline and ablation settings based on LoRA and ControlNet. The results indicate that knowledge-guided semantic control improves typological consistency, structural plausibility, and expert-rated semantic alignment while preserving visual diversity. This research demonstrates a practical pathway for coupling architectural heritage knowledge representation with generative AI, supporting cultural documentation, design assistance, and virtual reconstruction.