基于大数据的考研分数分析与报考预测系统设计与实现

摘要

考研已成为当前高校毕业生普遍选择的一项重要途径，但由于考研报名人数的不断增加，考研难度也越来越大。因此，为了更好地帮助考生做出选择，本文提出了一种基于大数据的考研分数分析与报考预测系统。该系统利用大数据技术对历年考研数据进行分析，得出各个专业的报考难度、分数线变化趋势等信息，为考生提供科学的报考建议。

本文首先介绍了考研分数分析与报考预测系统的研究背景和意义，然后详细阐述了系统的设计思路和实现方法。系统的主要功能包括数据采集、数据预处理、数据分析和报考预测等。其中，数据采集利用网络爬虫技术从官方网站获取历年考研数据；数据预处理包括数据清洗、数据去重、数据转换等操作；数据分析采用数据挖掘技术和机器学习算法对数据进行分析和挖掘；报考预测则基于历年数据和分析结果，利用数学模型和统计方法进行预测和推荐。

最后，本文对系统进行了实验测试，并对实验结果进行了分析和总结。实验结果表明，该系统能够准确地预测各个专业的报考难度和分数线变化趋势，并为考生提供科学的报考建议，具有很高的实用价值。

关键词：大数据；考研分数分析；报考预测；数据挖掘；机器学习

Abstract

The postgraduate entrance examination has become an important way for college graduates to choose. However, due to the increasing number of applicants, the difficulty of the postgraduate entrance examination is also increasing. Therefore, in order to better help candidates make choices, this paper proposes a postgraduate entrance examination score analysis and application prediction system based on big data. The system uses big data technology to analyze the postgraduate entrance examination data in previous years, and obtains information such as the difficulty of application for each major and the trend of score line changes, providing scientific application advice for candidates.

This paper first introduces the research background and significance of the postgraduate entrance examination score analysis and application prediction system, and then elaborates on the design ideas and implementation methods of the system in detail. The main functions of the system include data collection, data preprocessing, data analysis, and application prediction. Among them, data collection uses web crawler technology to obtain postgraduate entrance examination data from official websites; data preprocessing includes operations such as data cleaning, data deduplication, and data conversion; data analysis uses data mining technology and machine learning algorithms to analyze and mine data; application prediction is based on historical data and analysis results, using mathematical models and statistical methods for prediction and recommendations.

Finally, this paper conducts experimental tests on the system and analyzes and summarizes the experimental results. The experimental results show that the system can accurately predict the difficulty of application and the trend of score line changes for each major, and provide scientific application advice for candidates, which has high practical value.

Keywords: big data; postgraduate entrance examination score analysis; application prediction; data mining; machine learning