At present, self-adaptive software is providing the ability to adapt to the operating environment for many systems in different fields.How to establish a self-adaptation analysis method which can recognize abnormal events at runtime quickly and achieve the recognition quality assurance, is one of the research issues that must be considered to ensure the long-term stable operation of the self-adaptive software. The uncertainty of the runtime environment brings two challenges to this problem. On the one hand, the analysis method usually recognizes the events by pre-establishing the mapping relationships between the environment state and the events. However, due to the complexity of the operating environment and the unknown changes, it is impossible to establish comprehensive and correct mapping relationships based on experience before the system is running, which affect the accuracy of event recognition; On the other hand, the changing operating environment makes it impossible to accurately predict when and which event will occur. If the current way is used to obtain the environmental status using constant sensing period and recognize events, then the recognition efficiency cannot be guaranteed. However, it is still blank about how to deal with these urgent challenges. Therefore, this study proposes a self-adaptation analysis method for recognition of quality assurance using event relationships (SAFER). SAFER uses sequential pattern mining algorithm, fuzzy fault tree (FFT), and Bayesian network (BN) to extract and model the causalities between events. This study uses the event causal relationships and mapping relationships to recognize events through the BN forward reasoning, which can ensure the accuracy of recognition compared with the traditional analysis methods that only rely on mapping relationships. Moreover, this study establishes the elitist set of monitoring objects through the BN backward reasoning, then modifies the sensing period of monitoring objects in elitist set dynamically in order to obtain the environmental status as soon as possible after the abnormal events occurred, so as to ensure the efficiency of recognition. The experimental results show that SAFER can effectively improve the accuracy and efficiency of the analysis process, and support long-term stable operation of self-adaptive software.