[Objective] This study aims to address two core challenges in the digital governance of complex water networks: insufficient integration of physical topological relationships with multidimensional hydraulic characteristics, and limited multi-level routing analysis capabilities, which collectively weaken the support for water diversion decision-making. Focusing on the Beijing Section of the Middle Route of South-to-North Water Diversion Project and its supporting municipal projects, the research seeks to construct a knowledge graph that realizes the integrated expression of spatial topology and hydraulic attributes, and develop a scalable query system to meet diverse scheduling demands, thereby providing technical support for digital twin water network construction. [Methods] The research adopts a systematic approach for knowledge graph construction and application: (1) Knowledge modeling: A novel “topology-hydraulics-business” three-dimensional framework is proposed to integrate engineering entities, relational entities, and attribute entities, with core concepts aligned with the national standard SL/T 213—2020. (2) Knowledge extraction: Data from structured documents, semi-structured texts, and GIS spatial datasets are processed through standardization (unified units/coordinate system), missing value imputation, and synonym dictionary optimization to ensure data quality, followed by triple extraction using regular expressions. (3) Knowledge fusion: Entity alignment (resolving duplicates like “Miyun Reservoir Sluice” and “Miyun Reservoir Gate”), attribute integration (unified units: m³/s for flow, km for length), and relationship disambiguation (clarifying multi-semantic terms like “Yongding River”) are conducted manually. (4) Knowledge storage: The graph is stored in Neo4j for efficient visualization and querying. (5) Query system development: A three-level query system is built using Cypher language, integrated with the Dijkstra algorithm for path optimization. (6) Performance evaluation: Comparative experiments with Floyd-Warshall and SPFA algorithms are conducted to assess efficiency, convergence, and stability. [Results] The constructed knowledge graph achieves comprehensive semantic expression of the study area, encompassing 437 engineering entities, 149 relationship entities, and 6 attribute types. The three-level query system demonstrates strong applicability: (1) Basic topological analysis successfully retrieves direct downstream nodes of Guanting Reservoir (i.e., “Guanting Reservoir Sluice” and “Guanting Reservoir Spillway”) and the complete path of the Hexi Branch Line (from Daning Reservoir to Shimeng Gate Station via 3 pumping stations). (2) Conditional constraint filtering identifies 5 pipelines over 40km and generates an alternative path for water transfer from Guanting Reservoir to Zhuwo Reservoir when the spillway fails. (3) Multi-objective optimization yields optimal solutions: the shortest path between Huairou and Miyun Reservoirs is 30.33 km; the path with the highest water transmission efficiency (L/Q=3.658 5) from Tuancheng Lake to Huairou Reservoir; and the optimal path meeting dual constraints. Performance evaluation shows the Dijkstra-based algorithm outperforms competitors: average runtime of 0.05 s (vs. 1.655 s for Floyd-Warshall), average relaxation steps of 19 (vs. 34 for SPFA), and zero response time standard deviation, confirming high efficiency and stability. [Conclusions] This study makes two key innovations: (1) The “topology-hydraulics-business” three-dimensional modeling framework resolves knowledge fragmentation by integrating physical structure, hydraulic characteristics, and scheduling rules. (2) The three-level query system supports dynamic constraint filtering and multi-objective optimization, adapting to scenarios like emergency scheduling and daily management. The results validate the knowledge graph’s effectiveness in integrating multi-source data and supporting hierarchical decision-making, providing a replicable technical route for the digital governance of large-scale water diversion projects. Future research will focus on dynamic topology update mechanisms, logical modeling of complex scheduling rules, and human-machine interaction interface development, advancing the knowledge graph into an intelligent decision tool with spatiotemporal dynamic response capabilities to promote precision and intelligence in digital twin water network governance.