Most　of　the　existing　researches　on　public　health　events　focus　on　the　number　and　duration　of　events　in　a　year　or　month,　which　are　carried　out　by　regression　equation.　COVID-19　epidemic,　which　was　discovered　in　Wuhan,　Hubei　Province,　quickly　spread　to　the　whole　country,　and　then　appeared　as　a　global　public　health　event.　During　the　epidemic　period,　Chinese　netizens　inquired　about　the　dynamics　of　COVID-19　epidemic　through　Baidu　search　platform,　and　learned　about　relevant　epidemic　prevention　information.　These　groups＇　search　behavior　data　not　only　reflect　people＇s　attention　to　COVID-19　epidemic,　but　also　contain　the　stage　characteristics　and　evolution　trend　of　COVID-19　epidemic.　Therefore,　the　time,　space　and　attribute　laws　of　propagation　of　COVID-19　epidemic　can　be　discovered　by　deeply　mining　more　information　in　the　time　series　data　of　search　behavior.　In　this　study,　it　is　found　that　transforming　time　series　data　into　visibility　network　through　the　principle　of　visibility　algorithm　can　dig　more　hidden　information　in　time　series　data,　which　may　help　us　fully　understand　the　attention　to　COVID-19　epidemic　in　Chinese　provinces　and　cities,　and　evaluate　the　deficiencies　of　early　warning　and　prevention　of　major　epidemics.　What＇s　more,　it　will　improve　the　ability　to　cope　with　public　health　crisis　and　social　decision-making　level.