Under the background of urban expansion, ecological protection cannot be delayed. The construction of ecological networks is of considerable significance to ecosystem services. However, in the process of constructing a corridor network, there is no uniform standard for the selection of ecological sources and the determination of cost factors. The InVEST model is an effective complement to ecosystem service assessment for sensitively measuring external threats and their threat intensity. Therefore, taking Wuhan as an example, we combined InVEST and the least cost path model (LCP) to construct a multi-target corridor network with comprehensive cost factors for birds and small terrestrial mammals. The results showed that: (1) The InVEST model provided a reliable basis for ecological source screening by demonstrating the distribution of habitat quality. (2) The corridor with a length of 12–25 km presented a “U” shape, and the impact of urbanization on small terrestrial mammals was more significant than that of birds. (3) The integrated network pattern proposed by the “point-line-plane” principle would provide a reference for urban ecological construction and sustainable development.

To measure the equity of urban park green space, spatial matching between service supply and user group demand should be taken into consideration. However, if the demographic data, with the administrative division as the basic unit, are directly applied to characterize the spatial distribution of a user group, it may introduce inevitable deviation into the evaluation results due to the low-resolution nature and modifiable areal unit problem of such data. Taking the central area of Wuhan as an example, the population data spatialization method based on land use modeling was used to build a geographically weighted regression (GWR) model of land cover type and demographic data, and the spatial distribution of the population of the 150 m grid was obtained by inversion. Then, the equity of park green space in Wuhan central city was evaluated by population spatial data and network accessibility. The results showed that (1) the range of park green space in the central urban area of Wuhan was within a walking distance of 15 min, accounting for 25.8% of the total study area and covering 54.2% of the population in the study area; (2) the equity of park green space in Hongshan District was the worst; (3) and the use of population spatial data can measure equity on a more precise scale.

Urban park management assessment is critical to park operation and service quality. Traditional assessment methods cannot comprehensively assess park use and environmental conditions. Besides, although social media and big data have shown significant advantages in understanding public behavior or preference and park features or values, there has been little relevant research on park management assessment. This study proposes a deep learning-based framework for assessing urban park intelligent management from macro to micro levels with comment data from social media. By taking seven parks in Wuhan City as the objects, this study quantitatively assesses their overall state and performance in facilities, safety, environment, activities, and services, and reveals their main problems in management. The results demonstrate the impacts of various factors, including park type, season, and specific events such as remodeling and refurbishment, on visitor satisfaction and the characteristics of individual parks and their management. Compared with traditional methods, this framework enables real-time intelligent assessment of park management, which can accurately reflect park use and visitor feedback, and improve park service quality and management efficiency. Overall, this study provides important reference for intelligent park management assessment based on big data and artificial intelligence, which can facilitate the future development of smart cities.