In prenatal screening for fetal congenital heart disease (CHD), ultrasonic diagnosis and other methods are prone to being affected by regional resource differences and insufficient experience in diagnosing doctors, thus resulting in misdiagnosis of cases. This research puts forward a combined discriminative system, which integrates the iTransformer method and XGBoost to aid in the prenatal diagnosis of fetal CHD. This system, named INFO-iTransformer-XGBoost, merges a combined discriminative system, INFO (Weighted mean of vectors optimization algorithm), and SHAP (SHapley Additive exPlanations) explainable analysis prediction model. By comparing the model results with those from INFO-iTransformer and INFO-XGBoost alone, the study confirms the advantage of the combined discriminative system in prenatal CHD screening for fetuses. The study used the fetal CHD detection dataset provided by the Maternal and Fetal Medicine Center of Beijing Anzhen Hospital, Capital Medical University, from February 2018 to August 2024. The research shows that the INFO-iTransformer-XGBoost combined discriminative system and SHAP model explainability analysis can provide a quantitative diagnosis and clinically interpretable diagnostic solution for prenatal CHD screening.