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Based on the in\u2010depth study of the background, research status, related theories, and developments of online news text classification, this article analyzes the annual publication trend, subject distribution, journal distribution, institution distribution, author distribution, highly cited literature analysis, and research hotspots. Forefront and other aspects clarify the development context and research status of the text classification field and provide a theoretical reference for the further development of the text classification field. Then, on the basis of systematic research on text classification, deep learning, and news text classification theories, a deep learning\u2010based network news text classification model is constructed, and the function of each module is introduced in detail, which will help the future news text classification of application and improvement provide theoretical basis. On the basis of the predecessors, this article separately studied and improved the neural network model based on the convolutional neural network, cyclic neural network, and attention mechanism and merged the three models into one model, which can obtain local associated features and contextual features and highlight the role of keywords. Finally, experiments are used to verify the effectiveness of the model proposed in this paper and compared with traditional text classification to prove the superiority of the network news text classification based on deep learning proposed in this paper. This article aims to study the internal connection between news comments and the number of votes received by news comments, and through the proposed model, the number of votes for news comments can be predicted.<\/jats:p>","DOI":"10.1155\/2021\/8064579","type":"journal-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T20:20:05Z","timestamp":1624047605000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["[Retracted] News Text Classification Method and Simulation Based on the Hybrid Deep Learning Model"],"prefix":"10.1155","volume":"2021","author":[{"given":"Ningfeng","family":"Sun","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9200-3238","authenticated-orcid":false,"given":"Chengye","family":"Du","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,6,18]]},"reference":[{"key":"e_1_2_8_1_2","first-page":"797","article-title":"CSI: a hybrid deep model for fake news detection","volume":"31","author":"Ruchansky N.","year":"2019","journal-title":"Information and Knowledge Management"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jjimei.2020.100007"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/fi10110113"},{"key":"e_1_2_8_4_2","first-page":"185","article-title":"Text classification based on hybrid CNN-LSTM hybrid model","volume":"2","author":"She X.","year":"2018","journal-title":"Symposium on Computational Intelligence and Design (ISCID)"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2932619"},{"key":"e_1_2_8_6_2","doi-asserted-by":"crossref","unstructured":"AsimM. 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