Abstract: Nitrogen cycling in agroecosystems is directly related to food security and environmental protection and has received worldwide attention. Ecosystem nitrogen cycling involves the migration and transformation of nitrogen among various ecosystem components, characterized as integrity and complexity. However, existing studies have mostly focused on partial cycling processes and provide limited information about the changes in nitrogen cycling at the system level. By constructing an ecological network that simulates the flow of material or energy in a complex system, ecological network analysis (ENA) analyzes within-system interactions and system-wide properties from a holistic perspective. Therefore, applying ENA to examine agroecosystem nitrogen cycling as a whole has great application and development prospects. In this paper, we first introduce the principle and recent development of ENA, including the development of network particle tracking (NPT), which extends the application of ENA from steady-state to dynamic ecosystem models, and the application of NPT in developing new system-wide indicators, including the indirect effect index (IEI) and storage-based cycling index (SCI). Then, we introduce the main advantages of ENA and the primary procedures in the application of ENA and further demonstrate a case study of applying ENA to study nitrogen cycling in a rice field. Finally, we point out that the main problems hindering the application of ENA in agroecosystems are the difficulty and high cost of gathering empirical data required to build the network models and the scarcity of scientists who are knowledgeable in both ENA and the nitrogen cycling of agroecosystems. Regarding these problems, we provide possible coping strategies (e.g., building long-term experiments for data collection, making the best of historical data, developing more effective methods to facilitate network construction, and encouraging interdisciplinary cooperation) and further discuss the prospects of applying ENA in nitrogen cycling in agroecosystems.