To solve the problem of lack of market insight and insufficient innovation creativity in entrepreneurship of college students, this paper aims to explore the application of data mining and predictive analysis technology in this field. Quantitative research methods are employed. First, large‐scale data is collected, including relevant information, such as market trends, user needs, and competitor intelligence. Second, cluster analysis techniques are used to analyze and interpret these data. Third, historical data and features are leveraged to build predictive analytics models to identify potential market opportunities and innovations to evaluate the potential success of startup projects through predictive analytics. Finally, statistical analysis methods are used to quantify and compare data to obtain objective results. The results show that the number of users in specific fields is growing at a rate of 10% per year and is expected to reach 200 million in the next 3 years. Based on the predictive analysis model, the entrepreneurial success probability of the entrepreneurial project is evaluated, and the predictive model shows that the probability of project success is 80%. This data support helps entrepreneurs make informed plans, optimize project management and operational strategies, and provide more opportunities for college students to start their businesses. ig data–driven predictive analytics and business intelligence (BI) for enhancing college student entrepreneurship success. Therefore, the integration of big data and BI technology into college students’ entrepreneurship plays an important role, which can provide entrepreneurs with accurate market information and data support and improve the success rate of entrepreneurship.
Zhao et al. (2026) studied this question.