<p>Due to the rarity of credit default events and the intricate relationships among features, as well as the challenges associated with handling time series data, integrating diverse data sources, and addressing the operational complexities inherent in the credit risk prediction process, credit risk prediction in Chinese cross-border e-commerce enterprises (CBECEs) poses a formidable challenge. Providing an efficient and accurate credit risk prediction tool is an important and pressing need for the industry. In this paper, we propose an experimental framework for credit risk prediction in Chinese CBECEs. Our framework addresses these challenges by integrating oversampling methods to balance the dataset, employing feature processing techniques for feature selection and correlation handling, and ultimately applying a hybrid model, CNN-BiLSTM-AM model, for credit risk prediction. Experimental results on a real CBEC dataset demonstrate the effectiveness of the hybrid model, achieving high precision, recall, accuracy, and F1 scores. In summary, this research provides an effective approach for credit risk prediction in CBECs, offering valuable insights for enhancing risk assessment and decision-making. It aids enterprises in navigating uncertainties more effectively, formulating robust business decisions, improving operational efficiency, and thereby fostering the robust development and sustained growth of the industry.</p>