In　the　face　of　escalating　frequency　and　severity　of　hot　extreme　(HE)　events　worldwide,　understanding　their　spatio-temporal　characteristics　and　hazard　patterns　has　become　crucial.　This　study　employs　a　complex　network　(CN)　approach,　specifically　using　visibility　graph　and　similarity　network　analysis,　to　investigate　HEs.　According　to　the　HE　network,　we　have　successfully　identified　anomalous　years,　divided　stages　of　change,　selected　representative　cities,　and　zoned　spatial　hazard　patterns　of　HE.　Results　reveal　that　85%　of　cities　in　China　experienced　varying　degrees　of　increasing　HEs,　with　the　highest　increase　observed　as　63　times.　The　HE　networks　in　China　exhibit　small-world　characteristics,　allowing　the　classification　of　HE　changes　into　5-8　stages　and　10　types.　Hefei　emerges　as　the　most　representative　city　in　this　context.　Additionally,　the　hazard　of　HE　in　China　can　be　divided　into　four　grades,　with　a　gradual　increase　from　north　to　south.　This　study　sheds　light　on　the　intensifying　hot　extreme　events　in　China　and　establishes　a　connection　between　CN　and　HE　analysis,　offering　innovative　ideas　and　methods　for　studying　climate　extremes.