This paper develops an AR-based assembly guidance system, OpenIAI-SNIO, for small-scale, high-density industrial components (SHIC), which addresses the challenge of existing AR technology's inability to achieve complete, accurate, and stable visual cognition and assembly operation guidance for SHIC. OpenIAI-SNIO combines artificial intelligence methods such as computer vision and deep learning with rule-based reasoning and augmented reality to achieve adaptive, whole process, and precise guidance of SHIC assembly in situations where visual information is insufficient. The application case shows that OpenIAI-SNIO can effectively improve the efficiency and quality of SHIC assembly, and reduce the workload of operators, realizing the systematic and practical application of AR technology in SHIC assembly.