Text mining has not yet been fully explored in fisheries scientific literature and applications in existing studies have been limited. In the present study, quantitative text analysis was used in order to identify various subtopic trends and gaps in the knowledge of the fisheries science field. Several multivariate and descriptive analyses —including word extraction, word association, cluster analysis, co-occurrence network and correspondence analysis— were used for this purpose. Common words existed between periods, while each period was also characterized by unique words. Cluster analysis revealed four major thematic groups of words during the period 1971–2020. Categorization of words in eight major subsets highlighted the diachronically significant positively increasing engagement of literature with the ecological, economic and social dimension of fisheries science. A constant progress has been done in the field of fisheries management and in the use of functions/equations. Correspondence analysis indicated relationships between two decades, from 2001 to 2010 and from 2011 to present.

在渔业科学文献中尚未充分探索文本挖掘，并且在现有研究中的应用受到限制。在本研究中，使用定量文本分析来识别渔业科学领域知识中的各种子主题趋势和空白。为此，使用了一些多变量和描述性分析（包括单词提取，单词关联，聚类分析，共现网络和对应分析）。各个时期之间存在共同的词，而每个时期也具有独特的词。聚类分析揭示了1971-2020年期间的四个主要主题词组。在八个主要子集中对单词进行分类，突显出与生态学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学，文学等方面的积极互动 渔业科学的经济和社会层面。在渔业管理和职能/手段的使用方面已经取得了不断的进步。对应分析表明，从2001年到2010年以及从2011年至今，这两个十年之间存在着联系。