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Long short\u2010term memory (LSTM) processes the temporal characteristics of data and is mostly used for emotional text and speech recognition. Since an EEG involves a time series signal, this article mainly studied the introduction of LSTM for emotional EEG recognition. First, an ALL\u2010LSTM model with a four\u2010layered LSTM network was established in which the average accuracy rate for emotional classification reached 86.48%. Second, four EEG characteristics were extracted via the wavelet transform (WT) using the LSTM\u2010based sentiment classification network. The experimental results showed that the best average classification accuracy of these four features was 73.48%. This was 13% lower than in the ALL\u2010LSTM model, indicating that inappropriate feature extraction methods could destroy the timing of EEG signals. LSTM can be used to thoroughly examine EEG signal timing and preprocessed EEG data. The accuracy and stability of the ALL\u2010LSTM model are significantly superior to those of the WT\u2010LSTM model. The result showed that the process of emotion generation based on EEG is sequential. Compared with EEG emotion extraction using WT, the raw EEG signal\u2019s timing is more suitable for the LSTM network.<\/jats:p>","DOI":"10.1155\/2021\/8897105","type":"journal-article","created":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T18:23:11Z","timestamp":1612376591000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Construction and Analysis of Emotion Computing Model Based on LSTM"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5087-7589","authenticated-orcid":false,"given":"Huiping","family":"Jiang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7516-0469","authenticated-orcid":false,"given":"Rui","family":"Jiao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1569-9948","authenticated-orcid":false,"given":"Zequn","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7567-4403","authenticated-orcid":false,"given":"Ting","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5739-634X","authenticated-orcid":false,"given":"Licheng","family":"Wu","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,2,3]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/s1071-5819(03)00052-1"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10660-017-9265-8"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2018.09.002"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-73600-6_8"},{"key":"e_1_2_9_5_2","doi-asserted-by":"crossref","unstructured":"TanakaT. 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