Talent is the best group in human resources, and the Talents are the best group in human resources, and English-speaking talents are the most dynamic factor in productivity. In order to improve the quantitative analysis ability of the English talent aggregation effect, the English talents aggregation effect analysis model is proposed based on large-scale data collection technology. The collection information flow model of the hotspot big data of English talents aggregation effect is constructed. The high-dimensional feature grouping method is used to reconstruct the hotspot big data of the English talents aggregation effect. The piecewise linear test method is used to analyze the statistical characteristics of the hotspot big data of the English talents aggregation effect and extract the frequent vector set which reflects the hot big data category attribute of the English talents aggregation effect. According to the result of feature extraction, the fuzzy English talents aggregation is processed to realize the fusion of big data information of English talents aggregation effect hotspot. Combined with quantitative analysis method, the automatic classification of big data association rules is realized. The experimental and simulation results show that the proposed method is more accurate and effective than the traditional methods in collecting hotspot data, which is 26% and 76% higher than the traditional methods. This method has better accuracy and improved data aggregation effect in collecting hotspot data of English talents gathering and has strong collecting ability and characteristics. The research improved hotspot big data’s English talents gathering effect.

中文翻译：

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基于大数据热点采集技术的英语人才聚集效果研究

人才是人力资源上最好的群体，人才是人力资源上最好的群体，说英语的人才是生产力中最有活力的因素。为了提高英语人才聚集效应的定量分析能力，提出了基于大规模数据收集技术的英语人才聚集效应分析模型。建立了英语人才聚集效应热点大数据的采集信息流模型。高维特征分组法用于重构英语人才聚集效应的热点大数据。采用分段线性检验的方法，分析了英语人才聚集效应热点大数据的统计特征，提取了反映英语人才聚集效应热点大数据类别属性的频繁向量集。根据特征提取的结果，对模糊的英语人才聚集进行处理，以实现英语人才聚集效应热点的大数据信息融合。结合定量分析方法，实现了大数据关联规则的自动分类。实验和仿真结果表明，该方法在收集热点数据方面比传统方法更加准确，有效，分别比传统方法高26％和76％。该方法在收集英语人才搜集热点数据方面具有较高的准确性和改进的数据聚集效果，具有很强的搜集能力和特点。该研究提高了热点大数据的英语人才聚集效应。