The increasing complexity of energy systems in oilfields necessitates advanced approaches to monitor, analyze, and optimize energy usage. Traditional methods are often inadequate for processing the vast amounts of data generated from diverse sources, leading to inefficiencies in identifying and resolving energy consumption anomalies and making it difficult to achieve optimal energy utilization. To overcome these limitations and achieve the intelligent decision-making for energy management and control in oilfield gathering and water injection systems, an intelligent assisted decision-making method for abnormal energy consumption was proposed based on knowledge graph, addressing the challenges posed by massive multisource heterogeneous data. Specifically, the abnormal energy consumption records and operation manuals were utilized as the primary data source, and the comprehensive knowledge framework for energy management and control was established. This framework serves as the foundation for organizing and integrating multi-source data, ensuring systematic and efficient data utilization. Additionally, the BiGRU-CRF (Bidirectional Gated Recurrent Unit-Conditional Random Field) model was applied to extract entities from the textual data, identifying key concepts such as equipment, parameters, and anomalies. And the BiGRU-ATT (Bidirectional Gated Recurrent Unit-Attention) model was adopted to extract relationships between entities, capturing the complex interdependencies within the oilfield gathering and injection systems. The extracted energy consumption knowledge is stored and visualized using the Neo4j graph database, providing a robust platform for data querying and analysis. Its structured representation lays the foundation for the efficient utilization of data in subsequent stages. Finally, based on the constructed knowledge graph, an energy management and control visualization platform was developed, providing a user-friendly interface that enables operators to explore energy consumption data and knowledge in an intuitive manner, significantly enhancing the usability of the operational system. The platform provides actionable recommendations at both the data and knowledge levels, supporting energy consumption control effectively. The field application results in oilfields demonstrate that the proposed intelligent decision-making method, based on knowledge graphs, effectively integrates multi-source heterogeneous data for abnormal energy consumption detection in oilfield gathering and injection systems. Timely, comprehensive, and intelligent decision-making recommendations are provided for energy consumption anomaly events in the gathering and injection processes, guiding operators in achieving rapid and effective energy consumption control. The time required for decision-making is significantly reduced through this method. This study offers a novel and impactful approach for the construction of energy management and control systems in oilfields, which provides valuable guidance for the management of abnormal energy consumption in other oilfields.