In the construction of a smart marine, marine big data mining has a significant impact on the growing maritime industry in the Beibu Gulf. Clustering is the key technology of marine big data mining, but the conventional clustering algorithm cannot achieve the efficient clustering of marine data. According to the characteristics of marine big data, a marine big data clustering scheme based on self‐organizing neural network (SOM) algorithm is proposed. First, the working principle of SOM algorithm is analyzed, and the algorithm's two‐dimensional network model, similarity model and competitive learning model are focused. Secondly, combining with the working principle of algorithm, the marine big data clustering process and algorithm achievement based on SOM algorithm are developed; finally, experiments show that all vectors in marine big data clustering are stable, and the neurons in the output layer of clustering result have obvious consistency with the data itself, which shows the effectiveness of SOM algorithm in marine big data clustering.

中文翻译：

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基于自组织神经网络的海洋大数据聚类算法研究

在智慧海洋建设中，海洋大数据挖掘对北部湾日益壮大的海洋产业具有重要影响。聚类是海洋大数据挖掘的关键技术，但常规聚类算法无法实现海洋数据的高效聚类。针对海洋大数据的特点，提出了一种基于自组织神经网络（SOM）算法的海洋大数据聚类方案。首先分析了SOM算法的工作原理，重点介绍了算法的二维网络模型、相似性模型和竞争学习模型。其次，结合算法工作原理，开发了基于SOM算法的海洋大数据聚类过程及算法成果；最后，