On par with aerospace engineering, ocean engineering has caught a lot of attention re-cently. In this paper we employ machine learning and natural language processing methods to reveal new technologies and research hotspots in the ocean engineering field. Our data collection includes 14 high-impact journals, and the abstracts of almost 30,000 papers pub- lished from 2010 to 2019. We employed two topic models, Latent Dirichlet Allocation (LDA) and PhraseLDA. Used independently, the LDA model may lack interpretability and the PhraseLDA result may lose information in the final topics. We hence combined these two models and discovered the research hotspots for each year using affinity propagation cluster- ing and word-cloud-based visualization. The results reveal that several topics such as "wind power" and "ship structure", areas such as the European and Arctic seas, and some common research methods are increasing in popularity. This work consists of data collection, topic modelling, clustering, and visualization, which can help researchers understand the trends and important topics in ocean engineering as well as other fields.

中文翻译：

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使用机器学习揭示海洋工程研究中的新技术

与航空工程一样，海洋工程最近也引起了很多关注。在本文中，我们采用机器学习和自然语言处理方法来揭示海洋工程领域的新技术和研究热点。我们的数据收集包括14篇高影响力的期刊，以及2010年至2019年出版的近30,000篇论文的摘要。我们采用了两个主题模型，即潜在狄利克雷分配（LDA）和短语DLA。独立使用的LDA模型可能缺乏可解释性，PhraseLDA结果可能会丢失最终主题中的信息。因此，我们结合了这两种模型，并利用亲和力传播聚类和基于词云的可视化方法，发现了每年的研究热点。结果揭示了诸如“风力发电”和“船舶结构”之类的几个主题，欧洲和北极海域等一些常见的研究方法正在日益普及。这项工作包括数据收集，主题建模，聚类和可视化，可以帮助研究人员了解海洋工程以及其他领域的趋势和重要主题。