Assessing the visual quality of rural landscapes is pivotal for quantifying ecological services and preserving cultural heritage; however, conventional ecological indicators neglect emotional and cognitive dimensions. To address this gap, the present study proposes a novel visual quality assessment method for rural landscapes that integrates multimodal sentiment classification models to strengthen sustainability metrics. Four landscape types were selected from three representative villages in Dalian City, China, and the physiological signals (EEG, EOG) and subjective evaluations (Beauty Assessment and SAM Scales) of students and teachers were recorded. Binary, ternary, and five-category emotion classification models were then developed. Results indicate that the binary and ternary models achieve superior accuracy in emotional valence and arousal, whereas the five-category model performs least effectively. Furthermore, an ensemble learning approach outperforms individual classifiers in both binary and ternary tasks, yielding a 16.54% increase in mean accuracy. Integrating subjective and objective data further enhances ternary classification accuracy by 7.7% compared to existing studies, confirming the value of multi-source features. These findings demonstrate that a multi-source sentiment computing framework can serve as a robust quantitative tool for evaluating emotional quality in rural landscapes and promoting their sustainable development.

The visual quality assessment of rural landscapes is vital for quantifying ecological ser-vice functions and cultural heritage, yet traditional ecological indicators fail to capture emotional and cognitive experiences. Therefore, this study introduces a meth-od for assessing rural landscape visual quality that integrates a multi-modal emotion classification model to enhance the quantitative drive towards sustainability. The experiment selected four types of landscapes from three representative villages in Dalian, China, collecting physiological data (EOG, EEG) and subjective evaluations (beauty assessment and SAM scale) from participants. Binary, ternary, and five-element classification models were constructed. The results indicate that the bi-nary and ternary classification models yielded the highest accuracy in emotion valence and arousal, while the five-element model demonstrated the lowest performance. Additionally, ensemble learning models outperformed single classifiers in binary and ternary tasks, with an average accuracy improvement of 7.59%. Moreover, the collaborative fusion of subjective and objective data enhanced the accuracy of the ternary classification by 7.7% compared to existing research, confirming the efficacy of multisource features. The findings suggest that the framework based on multi-source affective computing can serve as a quantitative tool for assessing the emotional quality of rural landscapes and promoting the sustainable development of rural areas.

Present-day colours of historic buildings reflect inherited fabric and subsequent interventions. We analysed Roof, Facade, and Accent colours in 153 surviving buildings in Dalian, China, across four construction periods using CIELAB lightness (L*), CIELCh chroma (C*), Hue Family, district-adjusted models, and spatial statistics. Significant period associations occurred for Roof lightness (partial R² = 0.125, q = 0.003), Roof chroma (0.107, q = 0.005), and Facade chroma (0.115, q = 0.003), remaining significant after grouped-function adjustment. Roof Hue Family also differed by period (Cramér’s V = 0.359, q = 0.007). At k = 4, positive global spatial dependence occurred for Roof lightness (Moran’s I = 0.188, q = 0.030) and Facade chroma (I = 0.158, q = 0.011), but not Roof chroma. Two local Facade-chroma configurations were identified. Neither global association remained significant after period and district adjustment (both q = 0.939). The findings distinguish construction-period differentiation from spatial continuity and support component-level colour documentation, comparative conservation assessment, and future monitoring.

Historic-district maintenance and repainting may preserve familiar colors while altering relationships among roofs, facades, and architectural accents. Conventional palette surveys identify which colors occur but provide limited information about their component assignment and relational structure. This study develops a component-relational framework integrating architectural assignment, directional color differences, and recurring building-level configurations. The framework was applied to 153 selected historic buildings in Dalian, China; 149 complete roof–facade–accent cases were analyzed using normalized image-derived descriptors in CIELAB/CIELCh, CIEDE2000 color differences, repeated-measures tests, and exploratory clustering. The components occupied a shared warm-to-neutral gamut. Facades were generally lighter, whereas chroma showed no consistent ordering. Within the standardized dataset, roof–facade separation was lower than both roof–accent and facade–accent separation, while similar overall magnitudes arose from different combinations of lightness, chroma, and hue. Five overlapping configurations represented recurring relational centers rather than discrete styles. This study contributes a transferable component-relational method that extends palette inventories into comparative accounts of architectural color structure, supporting more precise analysis of historic-district color conservation.