The gas pipeline network, recognized as an indispensable component of modern urban energy supply systems, faces increasing operational challenges due to infrastructure aging and frequent leakage accidents associated with prolonged service durations. These emerging risks pose substantial threats to the stability and safety of pipeline operations, while the complex operational environment exacerbates existing deficiencies in data availability and interdepartmental coordination, particularly in accident prevention and emergency response mechanisms. An innovative knowledge graph-based early warning model for gas pipeline accident evolution was proposed, which effectively overcomed the limitations of conventional warning systems that predominantly rely on historical accident data and field monitoring information. A bidirectional long short-term memory network combined with conditional random fields (Bi-LSTM-CRF) was implemented to achieve high-precision causal chain extraction from gas pipeline accident narratives. The model's causal reasoning capability was further enhanced through the strategic integration of multidimensional feature information embedded within textual data. Subsequently, a comprehensive knowledge graph encapsulating complex causal relationships in gas pipeline accidents was systematically constructed using the Neo4j graph database platform. Predictive validation analyses were conducted through carefully selected case studies, with comparative results significant advantages are demonstrated over traditional warning methodologies. The occurrence probability of pipeline network accidents is successfully predicted, and their developmental trajectories and potential escalation pathways are precisely identified. A robust foundation is established for formulating emergency response strategies, and substantial decision-making support is provided for urban gas safety. The methodology and findings presented in this research offer novel perspectives for enhancing the operational reliability of critical energy infrastructure in smart city environments.