2023 Vol.6 Dec.N06

• Title: An association mining method based on LSTM algorithm for massive online news hotspots
• Name: Weiyao Li
• Company: Software College, Pingdingshan University, Pingdingshan 467000, China
• Abstract:

  At present, the conventional mass network news hotspot association mining method mainly realizes association mining by constructing vector space model and combining semantic analysis means, which leads to poor mining effect due to the lack of effective processing of data diversion. In this regard, we propose an association mining method based on LSTM algorithm for mass web news hotspots. Firstly, the key information features of the web page body are extracted, and combined with the crawler means, the directional and qualitative crawling as well as the storage of web pages are realized. Then the construction of data diversion mechanism is realized by using numerical prediction unit and error prediction unit to get the fitting error value of data. Finally, the hotspot data are mined and processed from four aspects, namely, average node weighted intensity, average path length, association division and feature vector center, respectively. In the experiment, the mining performance of the proposed method is verified. Finally, the experimental comparison results can prove that the algorithm has a lower false detection rate and leakage rate when the proposed method is used for network hotspot association mining, and has a more ideal data mining effect.

  
  

• Keyword: data mining; body text extraction; web crawler; hot news;
• DOI: 10.12250/jpciams2023090825
• Citation form: Weiyao Li .An association mining method based on LSTM algorithm for massive online news hotspots[J]. Computer Informatization and Mechanical System,2023,Vol.6,pp.115-119

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