The article aims to reduce PM_2.5 concentration by improving the spatial comfort of blocks and the vegetation configuration. It mainly analyzes the impact of the following five aspects on the PM_2.5 concentration distribution in blocks, including different angles between the prevailing wind direction and blocks, different vegetation types, the distance between vegetation and buildings, vegetation height and building height, and different street tree configuration types on both sides of the block. The results show that: when the street angle is 45 degrees, the PM_2.5 concentration in the air is the lowest. The PM_2.5 concentration in the air is significantly improved when the enclosed vegetation type (F1–F2) is planted, and the spacing between vegetation and buildings has no obvious effect on PM_2.5 concentration distribution. There is a negative correlation between the height of vegetation on both sides and the PM_2.5 concentration. At the height of 6 m, the PM_2.5 concentrations on the windward and leeward sides are relatively balanced. When the street trees are evenly distributed, they have the least effect on reducing PM_2.5 concentrations. However, the richer the distribution levels of street trees, the more obvious the effect on reducing PM_2.5 concentrations.

As urbanization and rapid industrialization accelerate, rural areas face increasing pressure on resources and the environment, leading to challenges such as energy waste and reduced comfort. Traditional village planning and design methods are based on economic benefits and often lack consideration of climate adaptability. To address these issues, a comprehensive assessment of building and courtyard performance should be introduced early in the planning of traditional villages. This approach can better adapt the buildings to their climatic conditions. Introducing relevant performance indicators, such as outdoor comfort, indoor lighting, and building energy consumption, at the initial design stage is crucial. This article employs performance-based multi-objective optimization algorithms and machine learning techniques to investigate the design workflow of courtyards and their combinations. The goal is to enhance planners’ design efficiency in village planning by integrating data-driven and performance-driven methods. The research results show that during the performance-driven phase, by adjusting the spatial morphology and architectural parameters, the performance of the courtyard significantly improved compared to the baseline model. Energy efficiency increased by 32.3%, the physiological equivalent temperature (PET) comfort time ratio in winter was enhanced by 8.3%, and the ratio in summer increased by 3.8%. During the data-driven phase, the classification prediction accuracy of courtyard performance can reach 83%, and the F₁ score is 0.81. In the project validation phase, it has also been proven that the performance of different plans can be quickly verified. Compared to the base’s original status, the design solutions’ performance score can be improved from 59.12 to 85.62. In summary, this workflow improves the efficiency of the interaction between design decisions and performance evaluation in the conceptual stage of village planning, providing a solid foundation for promoting subsequent solutions.