Research on Behavior Analysis and Employment of College Students Based on Big Data

The country regards the employment of college graduates as a top priority. There are phenomena of slow employment, delayed employment, difficulty in finding jobs, and difficulty in applying for jobs for college graduates. Tracking the behavior data of students from enrollment to graduation, integrating full caliber student behavior big data, strengthening data analysis, empowering the entire process of talent cultivation, and enhancing data to promote employment. Firstly, define big data, college student behavior, and college student employment, followed by the introduction of K-means clustering method and Apriori algorithm. Subsequently, a research model was established to collect and clean data. K-means clustering method was used to transform the data, and Apriori algorithm was used to explore the correlation between college students' behavior and employment. Finally, relevant conclusions were obtained. This can be achieved by regularly organizing employment related activities, emphasizing collaboration with enterprises, conducting employment ideological and political education in classroom teaching, encouraging students to participate in competitions, and adjusting talent training programs in a timely manner to help students improve their core competencies in employment.