摘要在科学研究过程中，科研人员需要考虑针对不同的研究问题选择合适的研究方法，有时还会对研究方法进行优化，从而更好地解决研究问题。因此，研究方法往往是解决研究问题的关键，是学术文献的重要知识。帮助科研人员快速发现学术文献全文内容中蕴含的方法实体，为其推荐适用于自身研究问题的关键解决方法提供实践参考，可以提高科研人员解决问题的效率。当前相关研究缺乏对方法实体之间共现关系的分析，未充分挖掘学术文献中蕴含的丰富知识。为此，本研究以自然语言处理领域为例，将方法实体细分为算法、数据集、指标以及工具4种类型，并标注了50篇论文作为训练语料。本研究构建了CRF（conditional random field）、BiLSTM（bi-directional long short-term memory）+CRF等4种实体抽取模型。研究结果表明，SciBERT（scientific bidirectional encoder representations from transformers）+CRF模型的性能最优。以ACL年会（Annual Meeting of the Association for Computational Linguistics）在2001—2020年共20年收录的论文全文数据为基础，进一步分析抽取出的方法实体的使用情况。本研究结合经典关联规则挖掘算法Apriori和卡方值构建方法实体共现数据集，并分析方法实体的演化。研究结果揭示了方法实体间的共现关系及其整体演化情况，可辅助特定领域的科研人员寻找合适的研究方法。
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