摘 要
随着信息技术的迅猛发展，社交网络已成为人们日常生活中不可或缺的一部分，用户在社交平台上的行为数据呈爆炸式增长。本研究旨在构建基于大数据技术的社交网络用户行为分析系统，以实现对海量社交数据的有效挖掘与智能分析。通过整合Hadoop分布式文件系统、Spark计算框架等大数据处理工具，结合机器学习算法，特别是深度学习中的卷积神经网络和循环神经网络模型，实现了对用户行为模式的精准识别。该系统能够实时采集多源异构社交数据，包括文本、图片、视频等多种类型，并进行预处理、特征提取与降维操作。研究结果表明，所提出的系统在用户兴趣预测、社交关系演化等方面展现出优异性能，准确率达到90%以上。
关键词：社交网络 用户行为分析 大数据处理技术
Abstract
With the rapid development of information technology, social networks have become an indispensable part of People's Daily life, and the behavioral data of users on social platforms has exploded. This study aims to build a social network user behavior analysis system based on big data technology, so as to realize the effective mining and intelligent analysis of massive social data. By integrating big data processing tools such as Hadoop distributed file system and Spark computing fr amework, combined with machine learning algorithms, especially the convolutional neural network and recurrent neural network models in deep learning, the accurate identification of user behavior patterns is realized. The system can collect multi-source heterogeneous social data in real time, including text, pictures, video and other types, and carry out pre-processing, feature extraction and dimension reduction operations. The results show that the proposed system shows excellent performance in the prediction of the user interest, social relationship evolution and other aspects, with an accuracy of more than 90%.
Keyword:social networks Big Data Processing Technology Deep Learning Model
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法概述 1
2社交网络数据获取与预处理 2
2.1数据来源与采集方式 2
2.2数据清洗与格式化 3
2.3数据标注与特征提取 3
3用户行为模式识别与分析 4
3.1行为特征定义与选取 4
3.2基于大数据的行为模式挖掘 5
3.3用户群体划分与画像构建 5
4系统设计与实现 6
4.1系统架构设计 6
4.2关键技术实现 6
4.3系统性能评估 7
结论 7
参考文献 9
致谢 10
随着信息技术的迅猛发展，社交网络已成为人们日常生活中不可或缺的一部分，用户在社交平台上的行为数据呈爆炸式增长。本研究旨在构建基于大数据技术的社交网络用户行为分析系统，以实现对海量社交数据的有效挖掘与智能分析。通过整合Hadoop分布式文件系统、Spark计算框架等大数据处理工具，结合机器学习算法，特别是深度学习中的卷积神经网络和循环神经网络模型，实现了对用户行为模式的精准识别。该系统能够实时采集多源异构社交数据，包括文本、图片、视频等多种类型，并进行预处理、特征提取与降维操作。研究结果表明，所提出的系统在用户兴趣预测、社交关系演化等方面展现出优异性能，准确率达到90%以上。
关键词：社交网络 用户行为分析 大数据处理技术
Abstract
With the rapid development of information technology, social networks have become an indispensable part of People's Daily life, and the behavioral data of users on social platforms has exploded. This study aims to build a social network user behavior analysis system based on big data technology, so as to realize the effective mining and intelligent analysis of massive social data. By integrating big data processing tools such as Hadoop distributed file system and Spark computing fr amework, combined with machine learning algorithms, especially the convolutional neural network and recurrent neural network models in deep learning, the accurate identification of user behavior patterns is realized. The system can collect multi-source heterogeneous social data in real time, including text, pictures, video and other types, and carry out pre-processing, feature extraction and dimension reduction operations. The results show that the proposed system shows excellent performance in the prediction of the user interest, social relationship evolution and other aspects, with an accuracy of more than 90%.
Keyword:social networks Big Data Processing Technology Deep Learning Model
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法概述 1
2社交网络数据获取与预处理 2
2.1数据来源与采集方式 2
2.2数据清洗与格式化 3
2.3数据标注与特征提取 3
3用户行为模式识别与分析 4
3.1行为特征定义与选取 4
3.2基于大数据的行为模式挖掘 5
3.3用户群体划分与画像构建 5
4系统设计与实现 6
4.1系统架构设计 6
4.2关键技术实现 6
4.3系统性能评估 7
结论 7
参考文献 9
致谢 10
