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.

This paper delves into the profound impact of NLP technology based on GPT model on the construction and development of intelligent libraries in higher education. The article firstly analyses the current development trend of intelligent libraries in higher education, and then elaborates the application practice of GPT model in key areas such as dialogue interaction, personalized resource recommendation, information processing and metadata management in libraries. Despite the benefits, challenges such as privacy concerns, semantic understanding limitations, domain-specific corpus requirements, and various user needs present obstacles. To address these, this research work proposes a comprehensive architecture incorporating secure data handling, optimized GPT model training, cross-domain knowledge integration, and user-friendly interfaces. Experimental testing of this architecture with university participants demonstrated a 66% reduction in query response times and a 21% increase in user satisfaction scores compared to traditional systems. These findings indicate significant improvements in accessibility, personalization, and operational efficiency. However, challenges related to data privacy and semantic accuracy were also identified. The proposed framework aims to utilize the capabilities of ChatGPT to enhance service efficiency, user satisfaction, and data management in intelligent libraries which support the evolving needs of students and educators.

Over the past decade, with the advancement of technology, chatbots have become a hotspot in the field of artificial intelligence (AI) and are widely used in consumer services, education, search engines, marketing, and other fields. Among them, the Chat Generative Pre-Trained Transformer (ChatGPT), composed of language models and optimization techniques, is leading a transformation in human-computer interaction methods. This study selects the social media platforms "REDnote" and "Weibo" as the research field to explore the role and impact of ChatGPT in education and industry ecosystems. This work examines public sentiment regarding the application of AI in educational ecosystems and talent development by analyzing social media discussions. The sentiment analysis conducted using advanced machine learning models, highlights the prevalence of positive emotions toward ChatGPT's role in enhancing teaching and learning experiences. Furthermore, this study introduces an AI-based dynamic talent cultivation model, rooted in the "3H" (Head, Hand, Heart) framework, which emphasizes cognitive skills, practical capabilities, and emotional intelligence.