Large weather models(LWMs) constructed using the ERA5 general dataset often face challenges such as numerous input features, high computational demands, and poor interpretability. Moreover, research on modeling rare extreme weather events, such as freezing rain, remains limited. To address these issues, taking the southern region with frequent freezing rain as the research subject, an optimal freezing rain model was developed using the extreme gradient boosting (XGBoost) algorithm and the Shapley additive explanations(SHAP) method to explore the forecasting mechanism of freezing rain. The results indicate that, based on research into freezing rain formation mechanisms, 48 meteorological features closely related to freezing rain are newly constructed from ERA5 data. Correlation analysis identifies 16 features as the key inputs, reducing the feature count by nearly 92% compared to the original dataset. Using feature importance analysis in combination with Bayesian optimization, an XGBoost-based optimal freezing rain prediction model is developed. This model outperforms existing freezing rain parameterization schemes, which combine physical mechanisms and statistics, with the seven selected key features showing clear physical relevance. The average importance of the model features is ranked as follows: 2-meter air temperature, altitude, atmospheric profile, proportion of solid precipitation particles, minimum temperature of the subfreezing layer, height of the top of the warm layer, and relative humidity at 600 hPa. SHAP values reveal the contribution of each feature to the prediction process and the model's forecasting mechanism. However, due to interactions between features, SHAP values vary across different times and locations, highlighting the complexity of freezing rain forecasting and the differing mechanisms at play in various temporal and spatial contexts. By incorporating only seven features, research results successfully develop an XGBoost-based freezing rain prediction model with strong forecasting capabilities and clear prediction mechanisms. This model provides valuable insights into forecasting low-probability extreme weather events, offering both scientific and practical value.