Abstract: Keywords in civil aviation can reflect synopsis of safety information.It is significant for security officers to extract and call information.An academic review of technologies to extract keywords is conducted in this paper.The features of keywords in civil aviation are analyzed.And a naive Bayes model for extraction of keywords is proposed.The selected features of this model are length of keywords;part of speech;frequency of words (including span of paragraph and position of words);and Term Frequency-Inverse Document Frequency (TF-IDF) value;which reflect the basic attributes of each candidate word.This model is trained by the safety information which has been manually labeled;in order to obtain the probability of each feature for extracting keywords.The probability of features is used to compute the score of all alternative words.The words with top three scores are regarded as keywords.Compared with the traditional TF-IDF algorithm and KEA algorithm;this method improves the accuracy by 18% and 11.9%;respectively.The recognition rate of words is also improved by 15.3% and 12.3%;respectively.The results show that;compared with other general methods;the accuracy and capability to recognize special words in civil aviation can be significantly improved by the method proposed in this study.