Constructing ecological networks was a crucial strategy for regional biodiversity conservation. Its technical strength lay in integrating ecological processes like species migration and dispersal while assessing the impacts of human activities on ecosystems. Research advancements and methodological innovations in this field held significant theoretical and practical importance for enhancing biological conservation. Based on the CNKI and Web of Science Core Collection (WoSCC) databases, this study retrieved and screened 300 papers (143 in Chinese, 157 in English) addressing ecological network research in China's biodiversity conservation from 2004 to 2024. Through bibliometric analysis, we systematically reviewed geographical distribution patterns, spatial scale classifications, ecosystem type differentiation, focal species selection, and network construction technologies. The aim was to reveal research hotspots and methodological limitations, providing a scientific basis for theoretical development and technological optimization in ecological network research. Key findings included: 1) Ecological network studies demonstrated significant growth trends, concentrated in flagship species-rich regions (e.g., Yunnan, Sichuan) and research-intensive urban clusters (e.g., Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD)). 72.8% of studies focused on county/municipal-level medium-small scales (1,000-50,000 km2), with urban ecosystems predominating (53%). 2) Approximately half of the reviewed literature failed to define focal species. Among studies specifying focal species, mammals constituted 53.62%, and 69.57% prioritized rare/protected species, such as the giant panda (Ailuropoda melanoleuca) and Asian elephant (Elephas maximus). 3) Methodology generally followed the "habitat identification - resistance surface construction - corridor extraction - ecological network optimization" framework. Corridor extraction primarily employed the Minimum Cumulative Resistance model (MCR, 63.3%) and Circuit Theory (32.3%). Network optimization often relied on identifying and restoring ecological pinch points and barrier points to enhance connectivity. Among the 86 studies conducting network evaluation, graph theory-based structural analysis (44.19%) and landscape pattern indices (31.40%) were predominant. This study identified existing limitations, including narrow focal species selection (neglecting common species), deficiencies in focal species data, and insufficient verification of conservation effectiveness. We recommended future efforts to: (1) broaden the scope of focal species in network construction and optimization, establishing composite conservation networks integrating "rare/protected species + common species", particularly focusing more on small-to-medium-sized native species in urban ecosystems; (2) explore generative artificial intelligence (GenAI) technologies to enrich focal species databases; and (3) further integrate low-cost novel technologies such as citizen science, crowdsourced data, and automated monitoring systems to build multi-species dynamic monitoring systems. These advancements would provide a scientific basis for forming China's biodiversity conservation ecological network and refining regional conservation policies.