The invention discloses a topic feature text
keyword extraction method. Through the method, text
keyword extraction results better than those of a traditional TF-IDF method can be obtained. Accordingto the technical scheme, at a training stage, word segmentation, stop word removal, part-of-speech filtering and other preprocessing are performed on a training text,
statistical analysis is performedon inverse document frequency of words, meanwhile a
topic model method is utilized to learn and obtain a topic probability matrix of the words, normalization
processing is performed, topic distribution entropy of the words is calculated according to the topic probability matrix of the words, global weights of the words are calculated in combination with the inverse document frequency and the topic distribution entropy, and global weight calculation results are output to a test stage; and after a test text is preprocessed,
statistical analysis is performed on normalized term frequency of wordsin the test text, the normalized term frequency is combined with the global weight calculation results obtained at the training stage, comprehensive scores of the words are calculated are ordered, and a plurality of words with the highest scores in the
score order are used as automatic
keyword extraction results of the current test text.