Purpose:　This　study　aims　to　explore　the　risk　spillover　effects　among　different　sectors　of　the　Chinese　stock　market　after　the　outbreak　of　COVID-19　from　both　Internet　sentiment　and　price　fluctuations.　Design/methodology/approach:　The　authors　develop　four　indicators　used　for　risk　contagion　analysis,　including　Internet　investors　and　news　sentiments　constructed　by　the　FinBERT　model,　together　with　realized　and　jump　volatilities　yielded　by　high-frequency　data.　The　authors　also　apply　the　time-varying　parameter　vector　autoregressive　(TVP-VAR)　model-based　and　the　tail-based　connectedness　framework　to　investigate　the　interdependence　of　tail　risk　during　catastrophic　events.　Findings:　The　empirical　analysis　provides　meaningful　results　related　to　the　COVID-19　pandemic,　stock　market　conditions　and　tail　behavior.　The　results　show　that　after　the　outbreak　of　COVID-19,　the　connectivity　between　risk　spillovers　in　China＇s　stock　market　has　grown,　indicating　the　increased　instability　of　the　connected　system　and　enhanced　connectivity　in　the　tail.　The　changes　in　network　structure　during　COVID-19　pandemic　are　not　only　reflected　by　the　increased　spillover　connectivity　but　also　by　the　closer　relationships　between　some　industries.　The　authors　also　found　that　major　public　events　could　significantly　impact　total　connectedness.　In　addition,　spillovers　and　network　structures　vary　with　market　conditions　and　tend　to　exhibit　a　highly　connected　network　structure　during　extreme　market　status.　Originality/value:　The　results　confirm　the　connectivity　between　sentiments　and　volatilities　spillovers　in　China＇s　stock　market,　especially　in　the　tails.　The　conclusion　further　expands　the　practical　application　and　theoretical　framework　of　behavioral　finance　and　also　lays　a　theoretical　basis　for　investors　to　focus　on　the　practical　application　of　volatility　prediction　and　risk　management　across　stock　sectors.　©　2023,　Emerald　Publishing　Limited.