Small and micro enterprises are pivotal in national economic and social development. To foster their growth, managing their credit risks scientifically is crucial. This study starts by examining the credit information of these enterprises. We use imbalanced sample processing algorithms to ensure a balanced representation of minority-class samples. Then, a machine learning classifier is employed to identify key factors contributing to these enterprises' low credibility. Based on these factors, an XGBoost scoring card model is developed. The study reveals: firstly, the integration of the SMOTE algorithm with the XGBoost model exhibits certain performance advantages in handling imbalanced datasets; secondly, trustworthy financial information remains at the heart of crucial risk determinants; thirdly, the XGBoost scoring card model based on significant features effectively enhances the accuracy of credit risk assessment. These insights provide both theoretical references and practical tools for enhancing the robustness of small and micro enterprises, facilitating early warnings on credit risks, and refining financing efficiency.

Technology-based small and micro enterprises play a crucial role in national economic and social development. Managing their credit risk effectively is key to ensuring their healthy growth. This study is based on corporate credit management theory and Wu’s three-dimensional credit theory. It clarifies the credit concept and measurement logic of these enterprises, considering their unique development characteristics in China. A credit evaluation system is constructed, and an innovative method combining machine learning with comprehensive evaluation is proposed. This approach aims to assess the credit status of technology-based small and micro enterprises in a thorough and objective manner. The study finds that, first, the credit level of these enterprises is currently moderate, with little variation. Second, financial information remains a key factor in credit evaluation. Third, the ML-AHP (Machine Learning-Analytic Hierarchy Process) combined weighting method effectively integrates subjective experience with objective data, providing a more rational assessment. The findings provide theoretical references and practical guidance for the healthy development of technology-based small and micro enterprises, early credit risk warning, and improved financing efficiency.