Abstract: The coordinated regulation of the Water-Ecology-Energy-Food (WEEF) nexus represents a critical pathway to achieving sustainable resource and environmental development under changing conditions. However, numerous systematic technical challenges persist across the analysis, simulation, optimization, and regulation stages. This study addresses these challenges through a comprehensive framework. In the analysis stage, it is imperative to develop methodologies integrating multi-source heterogeneous data fusion with high-dimensional causal relationship mining to achieve precise quantification of nonlinear interactions and feedback mechanisms among system components. In the simulation stage, multi-process, multi-scale dynamic coupling methods should be developed to construct high-performance integrated simulation platforms encompassing hydrological processes, energy systems, agricultural models, and ecological effects, thereby enhancing the capability to characterize system dynamic evolution. In the optimization stage, the computational efficiency and robustness of ultra-multi-objective optimization algorithms must be substantially improved, with particular focus on strategy optimization and risk quantification under deep uncertainty scenarios. In the regulation stage, this study explores adaptive control technologies leveraging digital twins and real-time feedback to achieve a transition from static schemes to dynamic intelligent decision-making. Looking forward,, WEEF nexus research is expected to exhibit three major developmental trends: ① Intelligence-driven transformation, harnessing generative artificial intelligence and big data to revolutionize system modeling and strategy generation paradigms. ② Integration and innovation, establishing modular model integration environments based on standardized interfaces and cloud-native architecture to facilitate rapid verification of multiple technical pathways. ③Decision empowerment, developing enhanced analytics and interactive visualization technologies to build immersive decision support systems. Ultimately, this study aims to establish an integrated "perception-cognition-prediction-regulation" technological framework to provide core scientific and technological support for regional and global sustainable development.