Abstract: Aimed at the problem that when false news detection is abstracted as a text classification task, the semantic information of news text may be ignored, a semantically enhanced false news detection method is proposed. The TextRank algorithm was used to extract the keywords of true and false news, and we integrated them into the original text for information enhancement. The ERNIE model was used to learn the semantic representation of knowledge enhancement, and extracted the local features of the news text through the CNN model, and input them to BiGRU to learn the sequence features. At the same time, the attention mechanism was introduced to highlight key feature words, and the feature vector was integrated with the semantic representation of knowledge enhancement before classification to realize false news detection. Experimental results show that the proposed method can effectively classify true and false news, and has a significant improvement in accuracy compared with common methods in false news detection tasks.