Analysis of Knowledge Structure of Digital Literacy of Finance Students Integrating LDA Topic Model and Big Data Mining

This paper focuses on the analysis of the digital literacy knowledge structure of students majoring in finance and proposes a research method that integrates potential Dirichlet allocation (LDA) theme models and big data mining technology. By collecting multi-source information such as learning data, online behavior data of finance major students, using data preprocessing technology to clean and convert data, using LDA theme models to extract knowledge topics related to digital literacy, and using the association rule algorithm in big data mining to analyze the relationship between knowledge. The research results reveal the core elements and internal connections of the digital literacy knowledge structure of financial major students, provide theoretical basis and practical guidance for optimizing digital literacy education in financial majors, and help enhance students' competitiveness in the digital economy era.