Confronting the age of artificial intelligence, exploring art through technology has become one of the directions of interdisciplinary development. Not only does artificial intelligence technology explore sustainability on a technical level; it can also take advantage of itself to focus on the visual perception of the living environment. People frequently interpret environmental features through their eyes, and the use of intuitive eye-tracking can provide effective data that can contribute to environmental sustainability in managing the environment and color planning to enhance the image of cities. This research investigates the visual responses of people viewing the historic city of Macau through an eye movement experiment to understand how the color characteristics of the physical environment are perceived. The research reveals that the buildings and plantings in the historic district of Macau are the most visible objects in the environment, while the smaller scale of St. Dominic’s Square, the Company of Jesus Square, and St. Augustine’s Square, which have a sense of spatial extension, have also become iconic environmental landscapes. This also draws visual attention and guides the direction of travel. The overall impressions of the Historic Centre of Macau, as expressed by the participants after the eye movement experiment, were mainly described as “multiculturalism”, “architectural style”, “traditional architecture”, “color scheme”, and “garden planting”. The 60 colors representing the urban color of Macau are then organized around these deep feelings about the environment. Therefore, for future inspiration, the 60 colors can be applied through design practice to create color expressions that fit the local characteristics, and thereby enhance the overall visual image of the city.

This study explored the overall visual elements of arcade buildings and streetscapes from the perspective of tourists and then evaluated their influence on the identity and emotion of places. The scope of the research was to take the arcade building in the port area of Shantou Small Park in Guangdong, China, and apply the eye-tracking experiment method and semantic difference method. This entailed collecting the eye-tracking data of 16 people, implementing 334 questionnaires for evaluation, verifying the correlation between the visual elements and place identity emotions, and then screening out the visual elements that affect place identity through correlation analysis and multiple linear regression analysis. The results show that plaque text morphological richness (p = 0.045 < 0.05, and the regression coefficient B = 0.146), the color brightness of plaque text (p = 0.031 < 0.01, and the regression coefficient B = 0.171), window morphological richness (p = 0.038 < 0.05, and the regression coefficient B = 0.106), window color brilliance (p = 0.047 < 0.01, and regression coefficient B = 0.094), and plant color brilliance (p = 0.029 < 0.05, and the regression coefficient B = 0.154) are five visual element evaluation factors that have a significant correlation with the identity and emotion of places. Although the fineness of architectural decoration is negatively correlated with place identity, it is not obviously significant. The research results provide reference for the visual control design of arcade buildings.

The resource allocation of charging stations is an important part of promoting the development of renewable energy in modern cities. It can promote the scientific and modern construction of urban resource allocation and promote the intelligent transformation of cities. In view of the existing problems in the resource allocation process of urban charging stations, such as a single planning method, considering the actual travel demand. Based on the smart city transportation network information, this article will consider the impact of charging station construction costs, user driving and waiting costs on the location of charging stations, construct a charging station configuration optimization model, and introduce charging convenience coefficients to modify the model. Secondly, this paper establishes a systematic clustering model based on principal component analysis, selecting factors such as per capita GDP, population, and civilian car ownership as indicators, clustering analysis of different regions and assigning different charging convenience coefficients. Finally, the shortest distance matrix between any two nodes is calculated by the Voronoi diagram to concentrate the regional charging load to the traffic node, and the Floyd algorithm is used to analyze and evaluate the effect of the established charging station configuration optimization model. This technology provides a basis for promoting the modernization of urban green transportation.

This study integrates space syntax and big data from the catering industry to explore the impact of grid and organic street patterns on the spatial distribution of restaurants from the perspective of urban morphology. Space syntax is a set of theories and techniques for the analysis of spatial configurations. Focusing on five areas of the Macau Peninsula, this study models urban forms using space syntax. Syntactic parameters and Dianping data are analyzed through geographic visualization, correlation analysis, and descriptive statistics. The results reveal that grid-patterned streets provide a relatively equitable commercial environment through a structured hierarchy, whereas organic-patterned streets foster commercial diversity via more complex accessibility patterns. Additionally, at the local network level, a “cultural layer network” mechanism is revealed in organically shaped streets, supporting the stable distribution of different types of restaurants within specific accessibility ranges. For the first time, this study employs high precision (street-level accuracy), multidimensional analysis (number of restaurants and number of reviews), and a systematic methodology (“form-function” research framework) within the same space syntax model to uncover the effects of different urban morphologies on restaurant distribution. Collectively, these findings highlight street morphology’s key role in shaping vibrant commercial street networks in rapidly urbanizing contexts, reveal the morphological–socioeconomic synergy underpinning local catering ecosystems, and offer robust empirical guidance for integrated urban renewal, planning, and design strategies.

Public art (PA) is a powerful tool for enhancing the environment in public spaces since it not only relieves the oppression caused by urban architecture but also enhances and beautifies urban public spaces. As DT (digital technology) develops so quickly nowadays, it offers PA significant technological assistance to achieve new functions, and changes in modern people’s aesthetic preferences and psychological makeup also present new demands for PA. This essay explores the use of PA design in the creation of urban landscapes from the viewpoint of DT. In order to do this, this paper suggests a method for measuring the environmental impact of urban landscapes that combines landscape measurement and semantic assessment. Full‐connection algorithm and greedy algorithm are introduced to identify urban functional types with high‐dimensional characteristics for the boundary tree classification model of high‐dimensional feature classification. The findings indicate that the public service area’s producer and user accuracy are, respectively, 0.8783 and 0.7833. It offers a fresh concept for the identification of urban functional zones while solving the issues of low identification efficiency and poor accuracy.

As a well-known World Cultural Heritage Site, the Historic Centre of Macao’s historical buildings possess a wealth of decorative patterns. These patterns contain cultural esthetics, geographical environment, cultural traditions, and other elements from specific historical periods, deeply reflecting the evolution of religious rituals and political and economic systems throughout history. Through long-term research, this article constructs a dataset of 11,807 images of local decorative patterns of historical buildings in Macau, and proposes a fine-grained image classification method using the ConvNeXt-L model. The ConvNeXt-L model is an efficient convolutional neural network that has demonstrated excellent performance in image classification tasks in fields such as medicine and architecture. Its outstanding advantages lie in limited training samples, diverse image features, and complex scenes. The most typical advantage of this model is its structural integration of key design concepts from a Transformer, which significantly enhances the feature extraction and generalization ability of samples. In response to the objective reality that the decorative patterns of historical buildings in Macau have rich levels of detail and a limited number of functional building categories, ConvNeXt-L maximizes its ability to recognize and classify patterns while ensuring computational efficiency. This provides a more ideal technical path for the classification of small-sample complex images. This article constructs a deep learning system based on the PyTorch 1.11 framework and compares ResNet50, EfficientNet-B7, ViT-B/16, Swin-B, RegNet-Y-16GF, and ConvNeXt series models. The results indicate a positive correlation between model performance and structural complexity, with ConvNeXt-L being the most ideal in terms of accuracy in decorative pattern classification, due to its fusion of convolution and attention mechanisms. This study not only provides a multidimensional exploration for the protection and revitalization of Macao’s historical and cultural heritage and enriches theoretical support and practical foundations but also provides new research paths and methodological support for artificial intelligence technology to assist in the planning and decision-making of historical urban areas.