Analysis of employment trends and prediction model for Chinese college graduates based on natural language processing

This study aims to address the employment challenges faced by Chinese college graduates by developing a predictive framework that integrates Natural Language Processing (NLP) with ensemble machine learning techniques. The methodology involves extracting features from both structured data (e.g., demographics, academic records) and unstructured text (e.g., job descriptions), followed by classification using Support Vector Machine (SVM), Bagging, and XGBoost algorithms. A weighted voting strategy is applied to fuse the model outputs. Experimental results using real-world data from 2010 to 2019 show that the ensemble model achieves 81.48% accuracy in employment type prediction and 81.53% in salary level prediction, outperforming individual models. These findings demonstrate that combining NLP-driven feature extraction with hybrid learning architectures can significantly enhance predictive performance. The study concludes that this integrated approach offers a powerful tool for analyzing employment trends and guiding graduate career planning. Practical implications suggest that universities and policymakers can adopt such models to improve employment guidance, tailor education to market demands, and enhance talent alignment strategies.