The monitoring and analysis of exceedance events within the flight operation quality assurance (FOQA) program is one of the crucial ways to identify potential risks in flights. However, existing methods lack comprehensive consideration for the interrelationships between various exceedance events, thereby failing to systemically quantify the risks associated with several interdependent events. This study introduces a risk assessment method for landing exceedance events by integrating association rule mining and quick access recorder (QAR) data. Firstly, based on the frequency of exceedance events and the findings of an expert survey conducted during the approach and landing phases of 9 799 flight segments, six exceedance events were found to be indicators of risk assessment: high normal acceleration at landing, low pitch at landing, long landing, excessive bank angle below 50 m, high speed below 50 m, and high descent rate (152～15 m). Secondly, we calculated the possibility and severity of the six indicators based on their correlations and deviations, respectively. Subsequently, a risk assessment model for landing was constructed using the cloud model. Lastly, the real QAR data were used to verify the model. The results can differentiate risk levels associated with specific exceedance events. In order to improve flight safety supervision and operational efficiency, this model offers a useful method for evaluating the exceedance risk during the landing phase.