This research presents a comprehensive study on employment trend analysis and forecasting for Chinese college graduates, leveraging Natural Language Processing (NLP) techniques. The study encompasses data collection from diverse sources, including resumes, job postings, and demographic data, followed by rigorous data preprocessing and feature engineering. NLP is applied to extract and analyze textual information from resumes and job postings, uncovering critical insights into skills, qualifications, and industry trends. Machine learning models are developed to predict employment trends, allowing for informed decision-making. Time-series analysis is employed to understand the evolution of employment patterns over time, revealing seasonal variations and long-term trends. Evaluation metrics are defined to assess the accuracy of predictions, ensuring the reliability of the forecasting model. The study not only offers valuable insights into the dynamic job market for Chinese college graduates but also considers ethical implications such as data privacy and bias. The results provide actionable recommendations for enhancing graduate employability and aligning education with industry needs. This research contributes to the growing field of NLP and machine learning applications in labor market analysis.