Emergencies　in　gas　pipeline　networks　can　lead　to　significant　loss　of　life　and　property,　necessitating　extensive　professional　knowledge　for　effective　response　and　management.　Effective　emergency　response　depends　on　specialized　knowledge,　which　can　be　captured　efficiently　through　domain-specific　lexicons.　The　goal　of　this　research　is　to　develop　a　specialized　lexicon　that　integrates　domain-specific　knowledge　to　improve　emergency　management　in　gas　pipeline　networks.　The　process　starts　with　an　enhanced　version　of　Term　Frequency-Inverse　Document　Frequency　(TF-IDF),　a　statistical　method　used　in　information　retrieval,　combined　with　filtering　logic　to　extract　candidate　words　from　investigation　reports.　Simultaneously,　we　fine　tune　the　Chinese　Bidirectional　Encoder　Representations　from　Transformers　(BERT)　model,　a　state-of-the-art　language　model,　with　domain-specific　data　to　enhance　semantic　capture　and　integrate　domain　knowledge.　Next,　words　with　similar　meanings　are　identified　through　word　similarity　analysis　based　on　standard　terminology　and　risk　inventories,　facilitating　lexicon　expansion.　Finally,　the　domain-specific　lexicon　is　formed　by　amalgamating　these　words.　Validation　shows　that　this　method,　which　integrates　domain　knowledge,　outperforms　models　that　lack　such　integration.　The　resulting　lexicon　not　only　assigns　domain-specific　weights　to　terms　but　also　deeply　embeds　domain　knowledge,　offering　robust　support　for　cause　analysis　and　emergency　management　in　gas　pipeline　networks.