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As per the requirements for cultivating high-quality talents, planned teaching staff construction and teaching reforms need to be carried out to promote teachers\u2019 appointments. Improving the system makes the appointment process more scientific by giving due attention to the individual characteristics of all types of teachers while hiring them for related jobs. The system motivates the love of teaching, high academic level, high teaching level, and competitive teaching. In recent years, the rapid development of artificial intelligence and deep learning caused many colleges and universities to put forward the target of campus digitization and education informatization. The state of the classroom is a critical reference factor throughout the teaching and learning process for evaluating students\u2019 acceptance of the course and the quality of the teaching. However, at present, the analysis of the classroom status is mainly conducted manually, which distracts teachers and is also not much precise. Therefore, finding a method that can improve the efficiency of classroom status analysis has great research significance. This study uses the deep neural network method to read each class\u2019s video recording and analyze it from the aspects of students\u2019 behavior and attendance. The system can realize class behavior and eventually evaluate the course quality employed to motivate teachers to improve teaching and overall quality of education.<\/jats:p>","DOI":"10.1155\/2021\/6275096","type":"journal-article","created":{"date-parts":[[2021,8,3]],"date-time":"2021-08-03T20:20:25Z","timestamp":1628022025000},"page":"1-8","source":"Crossref","is-referenced-by-count":8,"title":["Evaluation Model of Educational Curriculum in Higher Schools Based on Deep Neural Networks"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3175-1224","authenticated-orcid":true,"given":"Yong","family":"Jin","sequence":"first","affiliation":[{"name":"Department of Academic Affairs, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China"}]},{"given":"Yiwen","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China"}]},{"given":"Baican","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China"}]},{"given":"Yunfu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Organization, Yunnan University of Traditional Chinese Medicine, Kunming 650000, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2020.09930"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2019.06288"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.32604\/iasc.2020.010124"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.32604\/csse.2020.35.183"},{"issue":"3","key":"5","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.32604\/cmc.2020.010186","article-title":"A recommendation method for highly sparse dataset based on teaching recommendation factorization machines","volume":"64","author":"D. 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