Optimizing the layout of green infrastructure (GI) is an effective way to alleviate the fragmentation of urban landscapes, coordinate the relationship between urban development and urban ecosystem services, and ensure the sustainable development of cities. This study provides a new technical framework for optimizing GI networks based on a case study of Kaifeng, an exemplar of many ancient cities along the Yellow River in China. To do this, we used a morphological spatial pattern analysis (MSPA) methodology and combined it with Procedure for mAthematical aNalysis of lanDscape evOlution and equilibRium scenarios Assessment (PANDORA) model to determine the hubs of the GI network. Then we employed a least-cost path approach to simulate potential corridors linking the hubs. We further identify the key ‘pinch points’ of the GI network that need priority protection based on circuit theory. Altogether, this framework takes patches that have a high level of ecosystem services and are more important in maintaining overall connectivity as hubs, thereby improving the accuracy of hub identification. Moreover, it establishes a direct connection between GI construction and ecosystem services, and improves biodiversity conservation by optimizing the network structure of GI. The results of the case study show that this framework is suitable for GI planning and construction, and can provide effective technical support for the formulation of urban sustainable development strategies.

Urban green space, a vital part of urban ecosystems, offers inhabitants essential ecosystem services, and ensuring its fair distribution is essential to preserving their ecological well-being. This study uses Kaifeng City in Henan Province as the research object and aims to address the unique conflict between the preservation of well-known historical and cultural cities and the development of greening. It does this by integrating streetscape big data (2925 sampling points) and point of interest (POI) density data (57,266 records) and using the DeepLab-ResNeSt269 semantic segmentation model in conjunction with spatial statistical techniques (Moran’s Index, Locational Entropy and Theil Index Decomposition) to quantitatively analyze the spatial equity of the green view index (GVI) in Kaifeng City. The results of the study show that (1) The Theil Index reveals that the primary contradiction in Kaifeng City’s distribution pattern—low GVI in the center and high in the periphery—is the micro-street scale difference, suggesting that the spatial imbalance of the GVI is primarily reflected at the micro level rather than the macro urban area difference. (2) The distribution of the GVI in Kaifeng City exhibits a significant spatial polarization phenomenon, with the proportion of low-value area (35.40%) being significantly higher than that of high-value area (25.10%) and the spatial clustering being evident (Moran’s Index 0.3824). Additionally, the ancient city area and the new city area exhibit distinct spatial organization patterns. (3) POI density and GVI had a substantial negative correlation (r = −0.085), suggesting a complicated process of interaction between green space and urban functions. The study reveals that the fairness of green visibility in historical and cultural cities presents the characteristics of differentiated distribution in different spatial scales, which provides a scientific basis for the optimization of greening spatial layouts in historical and cultural cities while preserving the traditional landscape.