Practice Dilemmas and Policy Support for Family Child Care and Community Shared Parenting Models

This study addresses the quality assessment challenges in the digital transformation of childcare services, constructs a smart childcare quality assessment system based on big data analysis, and proposes an XGBoost prediction model optimized based on the exponential triangulation algorithm. By integrating machine learning technology and policy analysis framework, a dynamic assessment system connecting government regulation, agency operation and family demand is established. During the model construction process, feature engineering methods are used to extract multidimensional indicators of service quality, and nonlinear correlation analysis is combined to capture the complex mechanism of action in child care services. The experiments compare the performance of five typical machine learning models in the classification task, and the results show that XGBoost and the improved model have a significant advantage in the core metrics: the optimized model outperforms the native XGBoost in both accuracy (0.944 vs. 0.935) and recall (0.944 vs. 0.935), with an improvement in the F1 value of 1.1 percentage points, which indicates that the algorithm optimization effectively enhances the integration ability of feature space and the delineation precision of decision boundary. It is worth noting that although the improved model outperforms the native XGBoost in most classification metrics, XGBoost still maintains a slight advantage in the AUC value (0.961 vs. 0.951), which may stem from the inherent adaptability of its gradient boosting mechanism to the category-imbalanced data, especially when dealing with the low-probability samples, and demonstrates a stronger robustness. The study further reveals the nonlinear coupling law between policy elements and environmental variables in service quality assessment, and provides technical support for government departments to build a dynamic monitoring network with hierarchical classification by establishing the quantitative mapping relationship of quality characteristics-rating criteria-resource allocation.