In this study, we utilized the ArcGIS Geographic Information System (ArcGIS) to study the relevant datasets of park green and residential subsidiary green spaces in Wuhan City, and at the same time, scraped residential area prices data in Wuhan using big data technology. The inverse distance weight spatial interpolation method is employed to analyze the spatial patterns of residential area prices. Distances from each residential area to park green spaces at different scales are calculated to investigate their impact on residential area prices. Three strategies are proposed: the tailored allocation of different types of park based on regional characteristics, the implementation of “quantity guarantee and quality improvement” measures to enhance the quality of residential green spaces, and the promotion of interaction between park green and residential green spaces. These strategies aim to particularly uplift housing prices in economically disadvantaged areas, ultimately achieving shared prosperity.