Research on Optimizing NCAP Child Protection Safety Test System Using Machine Learning Intelligent Algorithm

This study proposes an enhancement strategy for the NCAP child protection safety evaluation system, leveraging intelligent machine learning algorithms. The core objective is to boost the system's accuracy in assessing the effectiveness of child seats and its operational efficiency. By employing a comprehensive learning framework and integrating multiple models, including random forests and gradient boosted decision trees (GBDT), this paper constructs a smart platform capable of autonomously identifying and predicting child passenger injury probabilities. The system is trained using extensive real-world test data and simulates child safety protection efficacy under diverse evaluation scenarios. Through feature selection and parameter tuning, the model's versatility is enhanced. Comprehensive simulations demonstrate significant improvements over conventional methods in terms of both accuracy and recall in predicting child injury risks. Sensitivity analysis and error verification confirm the system's stability and robustness under various conditions. This study underscores an intelligent algorithm improvement approach rooted in machine learning, significantly enhancing the NCAP child protection safety evaluation system's accuracy and demonstrating its broad applicability and superior performance through simulation outcomes.