A study on the relationship between career planning education strategies and student development based on big data analysis

In an environment where the number of college graduates is increasing year by year, the education of college students in career planning is particularly important. However, the practical aspects of teaching pose additional challenges. Based on big data analysis technology, this paper designs an optimization strategy for career planning education. We have established a joint school-enterprise model of career management for college graduates to streamline career matching in career planning education. We then use the improved SARIMA-BP neural network model to predict the trend of career demand, providing students with personalized career planning advice and employment guidance. Clear career planning, setting clear development goals, studying diligently, understanding one’s strengths and weaknesses, and understanding the demand for professional competence in career positions all demonstrate significant positive correlations, with correlation coefficients greater than 0.5. This indicates the necessity of a career planning education strategy based on big data analysis and its positive promotion effect on students’ development.