[目的/意义] 针对公安业务中对热线文本数据犯罪线索关键信息识别与筛查时存在的信息化分析能力不足问题，提出一种基于关键词挖掘的热线文本数据犯罪线索筛查方法，帮助业务部门提高相关情报研判效率，使得犯罪线索筛查工作更加信息化和科学化。[方法/过程] 考虑到直接采用文本类等算法方法或因有效信息样本量占比过小使得模型训练不充分，本文首先对已知犯罪线索进行基于文本相似度的种子词集抽取，然后采用Word2Vec对种子词汇从同类词、替代词两个角度扩展构成专业词库，最后使用基于语义的积分筛查模型实现对热线文本数据中犯罪线索筛查。[结果/结论] 对济南市1 050条先验热线文本数据作犯罪线索筛查实验，并进行实际比对与结果指标分析，得到结果召回率86%，可以认为本文所述基于语义的积分筛查方法对济南市热线文本数据内犯罪信息具体性识别达到预期效果并实现犯罪线索有效筛查。

[Purpose/Significance] Aiming at the problem of insufficient information analysis ability in the current public security business about identification and screening of crime clues in hotline texts, a method of hotline text data crime clue screening based on keyword mining is proposed to help business departments improve relevant intelligence and judgment [Method/Process] Considering that algorithms such as automatic text classification are subject to the problem of sample size, this paper firstly identified the key information of the known attribute data and established a seed lexicon, and then used Word2Vec to expand the seed vocabulary from the perspectives of similar words and synonym words to form a professional thesaurus, and finally used a semantics-based integral screening model to screen criminal clues in the hotline text data. [Result/Conclusion] This paper conducted a crime clue screening experiment on 1 050 priori hotline text data in Jinan City. After actual comparison and index analysis, the recall rate reached 86%. The specific identification of crime information in the text data of the city hotline achieved the expected effect and realized the effective screening of crime clues.