Construction of a Deep Learning-Based Model for Assessing College Students’ Employment Ability and Predicting Trends

The rapid evolution of the job market, influenced by technological advancements and changing industry demands, has led to a growing need for more accurate tools to assess and predict the employability of college students. Traditional methods of employability evaluation, often relying on static indicators such as academic scores, fail to capture the dynamic and multifaceted nature of student potential. This paper introduces a hybrid deep learning model that combines academic performance, extracurricular activities, internship experience, and social media engagement to provide a comprehensive employability assessment. By integrating these diverse data sources, the proposed model not only evaluates individual employability scores but also predicts employment trends across various industries, offering valuable insights into future labor market shifts. The experimental results demonstrate that the hybrid model significantly outperforms conventional machine learning algorithms in terms of accuracy, with a higher R-squared value and lower error rates. Additionally, the interpretability of the model, through feature importance analysis, highlights key factors such as GPA, internship experience, and social media activity, which strongly influence employability predictions. This research provides important implications for universities, employers, and students by helping them make informed decisions in navigating the evolving job market.