[目的/意义]科学高效的领域关键词筛选和提取方法，对于识别和分析领域知识的研究前沿与热点具有极大的帮助。[方法/过程]文章基于关键词同现网络，从个体统计信息、知识关联广度、关联关系质量、全网结构特征四个维度，对关键词提取方法进行比较研究。[结果/结论]研究结果表明，基于词频的关键词提取方法对网络鲁棒性的影响最大，适用于识别领域研究热点；基于度值的方法强调局部特征，适用于无权网络中识别领域核心知识；基于关系频次的方法重视关联关系的质量，适用于探索知识关联性的相关研究；基于特征分解的方法既考虑了网络全局的结构特征又兼顾了边的权重，具有综合的适用性。

[Purpose/significance] Scientific and efficient domain keywords screening and extraction methods are of great help in identifying and analyzing research fronts and hotspots in domain knowledge. [Method/process] Based on the keyword co-occurrence network, this article compares the keyword extraction methods from four dimensions: individual statistical information, knowledge association breadth, correlation quality, and network structure. [Result/conclusion] The results show that the keyword extraction method based on word frequency has the greatest impact on network robustness and is suitable for identify research hotspots. The method based on degree value emphasizes local features and is applicable to identify core knowledge in unweighted network. The method based on relationship frequency pays attention to the correlation quality, and is suitable for exploring related research of knowledge relevance. The method based on eigen decomposition considers both the global network structural characteristics and the weight of the link, and has comprehensive applicability.