In the era of rapid digitalization development, artificial intelligence (AI) technology has brought revolutionary changes to traditional work patterns. Based on Baidu Comate, this study proposed an automatic numbering method for sampling points in irregular geochemical networks. Automatic numbering tests, conducted on 12 000 geochemical sampling points, demonstrate that the method improved the efficiency by 99.8% and achieved 100% accuracy compared to the traditional manual method. This indicates that the proposed method is more efficient and accurate than traditional approaches, effectively avoiding human errors and improving work efficiency. This study also discussed the challenges AI faces in processing complex instructions, the importance of instruction clarity, the identification of complex logic, and the necessity of developing knowledge reserves. Although AI technology has significantly improved the efficiency and accuracy of the automatic numbering of sampling points in irregular geochemical networks, the early development of packaging tools requires personnel who can read codes to modify and verify the codes. Additionally, AI-assisted demand processing should be in phases, and ultimately, it is necessary to encapsulate verified codes into a tool for reuse.