With　the　advancement　of　science　and　technology,　an　increasing　number　of　industries　and　fields　are　striding　towards　the　informationization.　The　utility　tunnels　have　become　important　facilities　for　urban　energy　transmission.In　the　context　of　the　centralized　development　of　large-scale　municipal　infrastructure,　the　fire　hazards　of　utility　tunnels　have　gradually　become　prominent.The　convolutional　neural　network　established　by　YOLO　V5　can　perform　high-precision　fire　recognition,　and　then　is　able　to　extract　the　important　fire　parameters,　including　real-time　fire　spread　range,　the　fire　spread　speed　and　the　flame　width.By　designing　12　sets　of　fire　experiments　in　the　utility　tunnel　with　different　fire　development　speeds,　the　convolutional　neural　network　is　trained　and　verified　for　the　accuracy　of　its　parameter　extraction.The　results　show　that　the　average　relative　error　(ARE)　of　the　extracted　spread　position　of　fire　front,　fire　spread　speed　and　flame　width　are　around　5%~15%,　6%~20%　and　10%~27%,　respectively.Furthermore,　it　is　verified　that　the　method　can　guarantee　good　extraction　accuracy.For　informationization　of　building　fire　protection,　this　method　can　be　applied　to　formulating　the　fire　rescue　measures　at　the　fire　scene,　and　make　it　possible　to　examine　and　judge　the　fire　development　trends,　assess　the　severity　of　fire　accidents,　and　estimate　the　accident　losses　in　real　time.　©　2022,　Editorial　Office　of　China　Civil　Engineering　Journal.　All　right　reserved.