Studying　the　correlation　structure　of　the　stock　market　is　crucial　for　systemic　risk　and　portfolio　optimization.　We　construct　the　price　volatility　network　of　the　Shanghai　and　Shenzhen　300　index　components　in　China＇s　stock　market　in　the　period　2007-2020.　We　select　three　representative　major　emergencies:　the　global　financial　crisis　in　2008,　the　stock　disaster　in　2015,　and　the　COVID-19　epidemic　in　2020.　First,　we　find　that　when　the　stock　market　is　impacted　by　the　major　events,　the　network　shows　a　cluster　phenomenon.　The　cluster　effect　of　the　financial　crisis　events　is　smallest,　while　that　of　the　epidemic　events　occurs　most　rapidly.　Second,　the　key　nodes　in　the　stock　market　network　have　greater　risk　transmission　ability.　The　manufacturing　plays　a　crucial　role　during　the　later　stages　of　events,　while　the　financial　industry　plays　an　important　role　during　the　epidemic＇s　recovery　period.　Third,　the　network　structure　of　the　stock　market　has　an　indicator　effect　on　the　systemic　risk　contributions.　Generally,　the　greater　a　stock＇s　eigenvector　centrality,　the　greater　its　systemic　risk　contribution,　while　its　closeness　centrality　and　clustering　coefficient　have　opposite　effects.　The　study　has　important　enlightenment　significance　for　market　regulators　to　prevent　risk　diffusion　and　reduce　portfolio　risk　for　market　participants.