An Analysis of College Students' Deep Entrepreneurship Patterns Based on Spatio-Temporal Big Data Flow

Students' complicated behaviors often lead to fluctuations in the status of the job market and destroys the stable operation of the job market. This paper introduces the theory of spatio-temporal big data flow, constructs an adaptive student entrepreneurial behavior evaluation method for the status of job market, uses association rules reduction and association rules importance degree method to excavate the data of college students' entrepreneurial behaviors and job market status, analyzes the association degree of college students' entrepreneurial behaviors on job market status changes so to adaptively build the evaluation indicator and weight and accurately quantify the influence degree of students' entrepreneurial behaviors on job market status changes. The experimental results show that the evaluation method can help to accurately find out the students who caused the job market status changes and their behaviors, and effectively support the management and control of students' entrepreneurial behaviors.