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Machine Learning-Based Prediction and Analysis of Chinese Youth Marriage Decision
Jinshuo Zhang1, Chang Lu1,2, Xiaofang Wang1,3
1School of Psychology, Northeast Normal University, No. 5268, Renmin Street, Nan Guan District, Changchun 130024, China.
Behavioral Sciences (Basel, Switzerland)
|December 30, 2025
Summary
Work-life balance significantly impacts marriage decisions among Chinese youth. Factors like commute time and job satisfaction, analyzed via machine learning, reveal complex relationships influencing marriage likelihood.
Area of Science:
- Social Sciences
- Computational Social Science
- Demography
Background:
- Declining marriage rates in China present a significant societal challenge.
- Understanding the determinants of marriage decisions is crucial for policy development.
Purpose of the Study:
- To identify key factors influencing marriage decisions among Chinese youth.
- To provide data-driven insights for policymakers addressing declining marriage rates.
Main Methods:
- Utilized machine learning techniques on data from the China Family Panel Studies (2018-2020).
- Evaluated seven algorithms, with CatBoost identified as the most effective.
- Employed SHAP (SHapley Additive exPlanations) analysis to interpret model predictions.
Main Results:
- Work-related variables, particularly commute time and working hours, were the strongest predictors of marriage likelihood.
- Exceeding 50 minutes/hours of commute/work time negatively correlated with marriage.
- Job satisfaction exhibited a non-linear association with marriage decisions.
Conclusions:
- Work-life balance is a critical determinant in contemporary Chinese marriage decisions.
- Social decision-making, like marriage, involves complex and non-linear relationships.
- Findings offer evidence-based support for targeted policy interventions to address declining marriage rates.
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