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Data Analysis and Visualization of Food-Related Videos on Bilibili

Author(s)

Zehui Li1, Qiuchu Li1, QingFeng Zhou2

Affiliation(s)

¹College of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, China 2iFLYTEK Co., Ltd., Hefei, China

Abstract

In the current short-video content ecosystem, efficient analysis of massive data within vertical domains is crucial for content creation and platform operation. This project aims to conduct in-depth data analysis and visualization of food-related videos on Bilibili. Initially, the project utilizes HDFS for storing raw video data and employs the Spark big data processing framework for data cleaning, transformation, and statistical analysis, with the processed results imported into a MySQL database. Subsequently, the backend, built on Tomcat/Servlet and JDBC technologies, is responsible for database connection and providing structured data interfaces. The frontend adopts mainstream technologies such as Vue 3 and ECharts, retrieving data via Ajax to construct a fully functional web application. This application enables multi-dimensional visual analysis of food video content, UP Master engagement, traffic trends, and other key metrics, thereby offering data-driven insights and decision support for content creators and platform operators.

Keywords

Visualization;Spark; Vue; Bilibili Food Videos; Big Data Analytics; Servlet

References

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