The　effective　control　over　the　outbreak　of　COVID-19　in　China　showcases　a　prompt　government　response,　in　which,　however,　the　allocation　of　attention,　as　an　essential　parameter,　remains　obscure.　This　study　is　designed　to　clarify　the　evolution　of　the　Chinese　government＇s　attention　in　tackling　the　pandemic.　To　this　end,　674　policy　documents　issued　by　the　State　Council　of　China　are　collected　to　establish　a　text　corpus,　which　is　then　used　to　extract　policy　topics　by　applying　the　latent　dirichlet　allocation　(LDA)　model,　a　topic　modelling　approach.　It　is　found　that　the　response　policies　take　different　tracks　in　a　four-stage　controlling　process,　and　five　policy　topics　are　identified　as　major　government　attention　areas　in　all　stages.　Moreover,　a　topic　evolution　path　is　highlighted　to　show　internal　relationships　between　different　policy　topics.　These　findings　shed　light　on　the　Chinese　government＇s　dynamic　response　to　the　pandemic　and　indicate　the　strength　of　applying　adaptive　governance　strategies　in　coping　with　public　health　emergencies.