The current online sales supervision of E-cigarette is in a blank stage. This paper proposes an early warning model for illegal E-cigarette sales based on network public opinion analysis, using web crawlers and third-party API interface calls to obtain online maps, relevant merchant names on social platforms, Text data such as business content and user comments. Then design a deep learning model with a dual-channel four-layer architecture to conduct early warning analysis on online E-cigarette sales. Through experimental comparison, it is shown that the model proposed in this paper has good performance and can provide strong support for the supervision of E-cigarette sales.
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