Abstract
Industrial products are related to the safety of people's lives and property, and the detection and disposal of industrial product quality and safety emergencies is an important way to ensure the quality and safety of industrial products. The traditional detection methods of industrial product emergencies only consider the objective factors in the news text and ignore the factor of human subject status in the news communication. To solve the above problems, this paper proposes a multifeature fusion method for industrial product quality and safety emergency detection, which takes into account the number of likes, comments, forwarding and views in the process of event propagation and combines the basic weight and emergency weight to build a bursty word extraction algorithm to calculate the bursty degree. A similarity matrix is constructed based on word cooccurrence. The hierarchical agglomerative clustering algorithm is introduced to cluster the bursty words and realize the detection of news emergencies of industrial products. Compared with traditional emergency detection methods in the field of industrial products, it can effectively improve the accuracy of emergency detection, provide a reasonable basis for subsequent risk assessment, guidance and control of public opinion, and provide effective support for quality and safety supervision of the entire chain of industrial products.
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