基于Python的B站用户行为分析系统源码数据库论文

近年来，随着互联网技术的飞速发展和网络用户数量的快速增长，网络数据也呈现出爆炸式增长的趋势。这些海量的网络数据蕴含着巨大的商业价值，互联网企业通过对数据的分析可以更好地了解用户的需求，进而制定出更精准的经营策略，实现全面价值创造。因此，本研究旨在利用Python技术构建一套以B站数据为数据源的用户行为分析系统，通过大数据分析的手段对B站的UP主账号进行数据可视化分析。 在数据可视化方面，本系统可以通过对UP主账号的数据进行处理和分析，实现对UP主最喜欢发布的视频类型、视频标签以及UP主的粉丝数、获赞数等信息的柱状图可视化展示。此外，系统还会对用户观看视频的行为进行分析，包括观看互动性、观看分享性以及观看一键三连性等。通过这些分析，可以将粉丝榜、播放榜等数据进行均量和总量的图形展示，为有需要的企业提供数据支持，为对应UP主和用户的观影行为提供有效的数据分析参考。 关键词：大数据；数据分析；B站；视频类型分析 Abstract: In recent years, with the continuous development of Internet technology, the number of Internet users has grown rapidly, and the amount of network data has also increased exponentially. These network data can provide high commercial value for Internet companies. Internet companies can better understand user needs and make business decisions based on user needs through data analysis, and achieve value creation services for the entire company through targeted positioning. This study aims to develop a user behavior analysis system based on Python technology, using Bilibili data as the data source and using big data analysis to visualize the data of UP accounts in Bilibili. In the data visualization process, the system can analyze and visualize the favorite video types, video tags, as well as the number of fans and likes of UP accounts through bar charts and other graphical models. Additionally, the system will also analyze user behaviors such as interaction, sharing, and "one-click triple association" video types. Through this analysis, the system can provide graphical displays of fan rankings, play rankings, and other data in terms of average and total values. This provides data support for companies in need and offers effective data analysis references for UP accounts and user viewing behaviors. Keywords: big data, data analysis, Bilibili (B站), video type analysis