：文章应用自然语言处理的方法，对样本文献中的题名、摘要和全文进行分词，并连同关键词一起，分别提取了四种分析单元下的高频词并应用Pajek和Sci2两个软件工具和常用的八种指标（算法）分别进行了研究热点的识别。然后，以全文为效标，分别运用相关分析和配对样本t检验，对题名、摘要和关键词在研究热点识别上的同时效度进行了检验。研究发现：（1）基于摘要而识别的研究热点同时效度最高，而基于关键词所识别的研究热点同时效度相对较低，具有一定效度风险；（2）在研究热点的识别方面，文本比词的同时效度高，而且文本的长度对于同时效度有着一定影响。

Present study conducted a Natural Language Processing(NLP) on the titles, abstracts and whole paper of sample literature, together with keywords, to exact the high frequency words from all of above 4 units to identify the hot research issues via Pajek and Sci2. Moreover, present study performed Criterion-related Validity through correlation analysis and paired samples t-test by setting the hot research issues identified by whole paper as criterion and compared the correlation coefficient and t-value between whole papers and titles, abstracts and keywords to identify the Concurrency Validity of co-words analysis on hot research issues. The findings of this research include that: a) Those hot research issues identified by the abstract has a higher Concurrency Validity keywords. b) Aiming to identify the hot research issues, text is better than words from the perspective of Concurrency Validity, however, Validity is affected by the length of sampled text.