Abstract: COVID-19 has caused huge impact to financial system, among which stock market is one of the main sources of infection. This paper investigates stock market volatility based on complex networks, proposing the method of applying articulation point-targeted attack (APTA) in the model of the stock market volatility. The strategy of APTA is removing the most destructive articulation points (AP) that will result in most nodes disconnected from the giant connected component (GCC) by iterating, and eventually uncovering the residual giant bicomponent (RGB) that maintains the structural stability of the network. This paper models stock network based on realized volatility and thresholds, separates research period into steady-developing period and risk-fluctuating period, compares and analyzes the topological properties. Network centrality indexes and APTA are used to discover the important stocks that need to be supervised specially, thus avoiding the big-scale spread of risks or concurrent risks, and helping the supervisors maintain financial stability.