Predicting the geoeffectiveness of coronal mass ejections (CMEs) is a core issue in operational space weather forecasting. In recent years, deep learning methods have been increasingly applied to CME geoeffectiveness prediction. However, existing studies mostly rely on single data sources, with image-based and parameter-based approaches each having their own limitations and failing to achieve effective complementarity, while also facing challenges of high false alarm rates and extreme class imba