基于大数据技术的高等教育专业结构动态优化

H. Cui
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摘要

目前,以大数据采集、大数据分析与挖掘、机器学习等技术为核心的大数据技术已广泛应用于多个领域。大数据技术通过对海量数据的深度挖掘和分析,可以准确预测和判断学科专业人才的供需情况,从而为高等教育专业结构的动态优化提供有力支持。本文通过构建高校专业结构生命周期管理计划,确定大数据获取的来源,利用朴素贝叶斯分类器对行业人才状态数据进行分类描述,建立数据模型,统计当前专业技术人才分布情况;并采用时间序列预测方法预测企业各类专业技术人员未来需求趋势,为专业结构优化提供有力支持,从而实现高等教育专业结构动态优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Optimization of Specialty Structure of Higher Education Based on Big Data Technology
At present, big data technology with big data collection, big data analysis and mining, machine learning and other technologies as the core has been widely used in many fields. Through deep mining and analysis of massive data, big data technology can accurately predict and judge the supply and demand of disciplinary professionals, thus providing strong support for the dynamic optimization of the specialty structure of higher education. Through building life cycle management plan of specialty structure of universities and colleges, this article determines the source of big data acquisition, and by using Naive Bayes Classifier classifies and describes the talent status data of the industry, establishes the data model, and statistics the current distribution of professional and technical talents, and uses the time series prediction method to predict the future demand trend of all kinds of professional and technical personnel of enterprise, to provide strong support for specialty structure optimization, thereby to achieve higher education specialty structure dynamic optimization.
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