Marital Satisfaction Prediction Model Based on XGBoost: A Study on the Trade-off Between Housework and Economic Con-tribution

Traditional marital research relies mainly on questionnaires and qualitative analysis, making it difficult to effectively quantify contribution patterns and their impact on marriage. This study constructs a framework for predicting marital satisfaction based on artificial intelligence and information systems, integrating variables such as household contribution rate and family economic contribution rate, and using machine learning algorithms (XGBoost and deep neural networks) to model the data and optimise prediction accuracy. The experiment is based on marriage data collected by a distributed information system. The results show that economic contribution has a greater effect on short-term satisfaction, while household contribution has a more significant effect on long-term satisfaction. This study provides data support for electronic marriage counselling systems and promotes the development of intelligent management of marital relationships.