To address the severity of customer churn in the inclusive finance field and the shortcomings of the existing customer retention models in prediction accuracy and interpretability， a customer churn prediction model integrating Hierarchical Graph Neural Network （HGNN） and Specific Feature Learning （SFL）， HGNN-SFLN （HGNN-SFL Network）， was proposed to enhance the model’s prediction capability and understanding of feature interactions. Firstly， to address the data imbalance issue， an innovative hybrid sampling strategy was introduced， and feature-level weighted adjustments for different feature categories were implemented to ensure the effective utilization of all data types. Secondly， a hierarchical graph was utilized to strengthen correlations between different features， and an SFL module based on a self-attention mechanism was constructed to improve the model’s ability to process categorical features and analyze feature interaction relationships. Through this module， accurate identification of key features and effective capturing of complex interaction relationships between them were enabled by the model， thereby optimizing the prediction decision-making process. Experimental results demonstrate that the proposed model achieves optimal results on multiple real-world financial datasets compared to mainstream models such as Light GBM （Light Gradient Boosting Machine） and Deep Neural Network （DNN）in key indicators such as Area Under Curve （AUC）. Furthermore， the proposed model has significant advantages over the comparison models in the accurate identification of critical churn-related features and the effective capturing of complex feature interaction relationships.