燃气管网是现代城市能源供应体系中不可或缺的一环，然而随着使用年限的增长，燃气管网基础设施不断老化，燃气泄漏事故频繁发生，对燃气管网的稳定与安全运营构成了严峻挑战与潜在风险。面对复杂多变的运行环境，城市燃气管网的运营与紧急响应面临着数据匮乏、多部门协作不畅等难题，尤其在事故预防与应急处理方面显得尤为突出。本文提出了一种基于知识图谱的燃气管网事故演化预警模型，突破了传统预警系统对现场监测数据和历史事故数据的过度依赖的难题。运用双向长短期记忆网络联合条件随机场模型（bidirectional long short-term memory-conditional random field, Bi-LSTM-CRF），高度精确地从燃气管道事故叙述中解析出因果链，同时，整合文本中的多维度特征信息，旨在进一步增强因果抽取的效能与精确度。然后，借助Neo4j图数据库，构建了涵盖复杂因果关系的燃气管网事故演化知识图谱。最后，通过选取具体案例进行了预测性的验证分析。预测结果显示，与传统的预警方法相比，该模型不仅能够有效预警燃气管网事故的发生，更能精准把握事故的发展趋势和潜在路径，为紧急响应策略的制定提供坚实依据与强大助力。

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.