Scenarios are effective mechanisms for analyzing the occurrence, development, and possible consequences of an accident. However, lack of effective model to model or limitation of models to analysis, scenario-based early warning mechanisms are difficult to popularize in practice. Abstract fault tree is a high-level abstraction of the same kind of fault tree. Based on historical cases and expert experiences, it can characterize the mechanism, evolution process, and possible consequences of the accident, and can effectively support scenario-based early warning analysis. A method of early warning of chemical accidents based on abstract fault tree is proposed. Based on the abstract map relation, hazard degree, and importance level of nodes are calculated. The scenario-evolved cutting set model is transformed into Bayesian network model. Board method is used to measure risk of accident hazard. The ranking of defense events can be used to predict the accident risk and propose the best coping strategies based on different evolution paths of scenarios. The experimental results show the effectiveness of this method in accident analysis and early warning.