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The massive reports of fault events collected by the civil aviation safety reporting system are stored in the form of unstructured texts, which are not convenient for the relevant personnel to analyze and take control measures for a large number of abnormal events. Named entity recognition technology can detect and identify the key elements in the massive unstructured texts and extract them into structured information with clear categories, which can be used as the foundation work for further analysis and control of abnormal events. As the Airport abnormal events reports are taken as the research object, a neural network-based Chinese named entity recognition model is proposed to structure the texts. For the problems of sparse distribution of some entity categories of randomly selected training samples and time-consuming and laborious manual labeling, a sample selection strategy based on model prediction scores is proposed to achieve efficient screening of pre-labeled samples. After experimental validation, the model improves the F1 value by about 6 percentage points compared with the BiLSTM_CRF model and BiLSTM_self-attention_CRF model, and this sample selection strategy significantly improves the manual annotation efficiency and screens out enough samples containing sparse entities.