<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">ETR</journal-id><journal-title-group><journal-title>Educational Theory and Research</journal-title></journal-title-group><issn>2995-3448</issn><eissn>2995-3456</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/ETR.2026210023</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>深度学习赋能研究生课程改革的探索与实践</title><url>https://artdesignp.com/journal/ETR/4/21/10.61369/ETR.2026210023</url><author>安杰,张勤进,刘厶源</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>4</volume><issue>21</issue><history><date date-type="pub"><published-time>2026-05-22</published-time></date></history><abstract>随着&amp;ldquo;海洋强国&amp;rdquo;战略实施及AI 技术爆发，智能航运对高层次人才提出新挑战。针对传统航海研究生课程理论晦涩、与海洋场景适配度低及实践不足等痛点，本文以《深度学习理论与船舶海洋工程应用》课程为例，基于成果导向教育理念提出了一套深度学习赋能的课程改革方案。构建了&amp;ldquo;数理基础&amp;mdash;核心算法&amp;mdash;行业场景&amp;mdash;前沿实战&amp;rdquo;的进阶式课程体系，创新引入生成式AI 辅助与虚实孪生演练教学手段，并设计全周期评价机制。该模式能够有效消除学科壁垒，提升研究生利用深度学习解决智慧船舶与智能运维等复杂工程问题的能力，为培养交叉学科背景的复合型智能航运人才提供了可复制范式。</abstract><keywords>深度学习,船舶海洋工程课程,进阶式课程体系,多元化人才教育</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1]Hasan S. An insight on the role and response of International MaritimeOrganization and classification societies in regulating maritime autonomous surfaceships[J]. International Journal of Maritime History, 2025, 37(2): 350-371.[2]Garc&amp;iacute;a-Llave, R., Andrade, F.H.E. &amp;amp; Huertas, D.J.C. Autonomous ships andflag state: challenges and opportunities in international maritime law[J]. Journal ofTransportation Security, 2025, 18: 15.[3] 交通运输部, 中央网信办, 国家发展改革委, 教育部, 科技部, 工业和信息化部,财政部.. 智能航运发展指导意见 ( 交海〔2019〕66号). 2019.[4]LeCun, Y., Bengio, Y., &amp;amp; Hinton, G. Deep learning[J]. Nature, 2015, 521(7553):436-444.[5] 潘文寅, 袁梦, 李卫锋, 等. 基于深度学习的船载运动监测决策系统开发与应用[J].舰船科学技术, 2025, 47(1): 177-184.[6] 林健., 面向未来的中国新工科建设[J]. 清华大学教育研究, 2017, 38(2): 26-35.[7]Li W, Su N. AI-Enhanced Cultivation of Students&amp;lsquo; Innovative and PracticalAbilities in Industry-Specific Universities: A Case Study of Naval Architecture andOcean Engineering[C]//Proceedings of the 5th International Conference on Internet,Education and Information Technology (IEIT 2025). Atlantis Press, 2025, 539-546.[8] 邢鹏飞, 杨杰, 马来好, 等. 轮机工程专业智能故障诊断实验教学项目设计与应用[J].航海教育研究, 2025, 42(3): 27-33.[9] 仝瑶, 黄曙路, 赵晓明, 孙保胜, 贺晋, 阮锦佳, 基于双边频域空间域特征增强的水面目标检测算法[EB/OL].(2025-06-17)[2026-02-12].http://www.paper.edu.cn/releasepaper/content/202506-48.[10]Yanlin L., Huibing G., PFGAF and MSPCANet-GCL: A general faultdiagnosis method for marine machinery components with data imbalance[J], OceanEngineering, 2025, 333: 121497.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
