我国经济和社会水平在改革开放的推动下持续提升，正稳步迈向第二个百年奋斗目标。在这一大背景下，就业作为民生的重要基石，其稳定性对于国家经济的健康发展和社会稳定至关重要。员工流失问题对企业运营和竞争力有着不可忽视的影响，因此深入研究和准确预测员工流失成为了当前亟待解决的课题。本研究采用机器学习算法构建模型，包括Lasso回归、逻辑回归和XGBoost，通过对比不同模型的综合性能，XGBoost模型的分类模型效果最佳，实现了对员工流失风险的有效预测。在影响因素的分析上，本文综合考虑了月收入、年龄、上班距离、加班状况、薪酬增长幅度、在职时间、员工股权、工作环境满意度以及过往工作经历等多个方面，并利用XGBoost模型进行各变量的重要性可视化展示。本文还揭示了这些因素与员工流失之间的内在联系，提高员工的月收入和改善工作环境满意度有助于减少流失，而频繁加班、薪酬增长缓慢以及较短的在职时间则可能增加流失风险。基于上述研究，我们为企业提供了针对性的管理建议，旨在降低员工流失率，增强企业的稳定性和市场竞争力。此外，本研究不仅丰富了员工流失预测领域的理论探索，也为企业制定科学、合理的人力资源管理策略提供了有益的参考。通过构建预测模型和分析影响因素，企业能够更准确地识别流失风险，制定干预措施，进而提升整体运营效率和市场竞争力。

Driven by reform and opening-up, China's economic and social levels have been continuously improving, steadily moving towards the second centenary goal. Against this backdrop, employment, as an essential cornerstone of people's livelihood, plays a crucial role in the healthy development of the national economy and social stability. Employee turnover has a significant impact on the operation and competitiveness of enterprises, making the in-depth study and accurate prediction of employee turnover an urgent issue to be addressed. This study employed machine learning algorithms, including Lasso regression, logistic regression, and XGBoost, to build models. By comparing the overall performance of different models, the XGBoost model was found to be the most effective in classifying and predicting the risk of employee turnover.In the analysis of influencing factors, this study comprehensively considered various aspects such as monthly income, age, commuting distance, overtime status, salary growth rate, tenure, employee stock ownership, job satisfaction, and past work experience. The XGBoost model was utilized to visualize the importance of each variable. This study also revealed the intrinsic relationship between these factors and employee turnover. Increasing employee monthly income and improving job satisfaction contribute to reducing turnover, while frequent overtime, slow salary growth, and shorter tenure may increase the risk of turnover.Based on the above research, we provided targeted management suggestions for enterprises, aiming to reduce employee turnover rates and enhance the stability and market competitiveness of enterprises. In addition, this study not only enriched the theoretical exploration in the field of employee turnover prediction but also provided a useful reference for enterprises to formulate scientific and reasonable human resource management strategies. By building prediction models and analyzing influencing factors, enterprises can more accurately identify turnover risks, formulate intervention measures, and thereby improve overall operational efficiency and market competitiveness.