Recently, Nature reported on the research findings of a team from the University of Kansas. They developed an AI-generated content detection system specifically for the introduction sections of chemical journal papers, which can distinguish between human-written and AI-generated texts with an accuracy rate of 98% to 100%. The researchers extracted 20 key linguistic features and used the XGBoost model for training, achieving high-precision detection. In contrast, general AI detectors like OpenAI and ZeroGPT have accuracy rates ranging from 10% to 65%. The research team stated that by constructing customized detectors for specific text types, they can lay the foundation for developing general detectors. This customized detector is also effective for content generated by the new version of GPT-4. The study provides an effective technical pathway for curbing the proliferation of AI-generated content.