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Compared with deep neural network, the proposed method could be more accurate with various periods and time intervals. We also predict different models for different kinds of paper. In this way, the model is more adaptable. The experiments demonstrate that our method is 69.8% better than benchmark algorithm in accuracy rate.<\/jats:p>","DOI":"10.3233\/jcm-226625","type":"journal-article","created":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T13:29:41Z","timestamp":1670333381000},"page":"1115-1123","source":"Crossref","is-referenced-by-count":0,"title":["Topic selecting approach of academic journals based on research focus"],"prefix":"10.66113","volume":"23","author":[{"given":"Yan","family":"Ma","sequence":"first","affiliation":[{"name":"State Grid Shandong Electric Power Research Institute, Jinan, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lida","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingkun","family":"Han","sequence":"additional","affiliation":[{"name":"State 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