Research on Cultural Characteristics and Environmental Adaptability in Public Space Using Artificial Intelligence-Assisted Design

As the main activity place to meet the public needs of residents in the city, the overall design of public space presents cultural characteristics and environmental integration, which is closely related to the satisfaction of the residents and the appearance of the city. This paper proposes a collaborative filtering-based public space layout algorithm by combining the distribution probability of design features and user interest characteristics. The deep neural network is used to extract the features of different spatial functional requirements and spatial layout parameters from the distribution structure and distribution pattern of the layout space. After obtaining the overall layout scheme and layout parameter features, the optimal layout scheme is determined by minimizing the energy function in the layout method. The graph-driven MH layout optimization (GD-MH) algorithm is improved to optimize the spatial layout, and a public space layout design method based on the GA-MH algorithm is formed comprehensively. The designed method is able to optimize the layout scheme with an interference of 0mm compared with similar methods.