[Purpose/significance] Aiming at the problem that the existing weak signal automatic recognition research is still incomplete, this paper proposes a weak signal automatic recognition method based on the LDA-BERT fusion model.[Method/process] Based on the unsupervised LDA topic model, the text data set was classified by topic, and the topic and term double-layer filter function was constructed to extract early warning signals from the results of topic classification.The weakness of the topic was evaluated by the three major metrics of close centrality, topic weight and topic autocorrelation, and weak signals were extracted based on the normalized frequency and probability of terms within the topic. Finally, the BERT deep learning model was used to expand the weak signal context and similar words from the semantic level.[Result/conclusion] Taking the re-eruption of the epidemic in early January 2021 as an example, the constructed system model was verified using the social media news data set of the three months before the outbreak. The experimental results show that the method can effectively detect the relevant weak signals and dig out the evolution characteristics of the weak signals that gradually increase over time. In addition, the fusion model not only realizes the automatic identification of weak signals, but also shows stronger result interpretability than a single model.
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