As ChatGPT and other foundation-model-based products demonstrate powerful general performance， both academia and industry are actively exploring how to adapt these models to specific industries and application scenarios， a process known as the customization of domain-specific foundation models. However， the existing general-purpose foundation models may not fully accommodate the patterns of domain-specific data or fail to capture the unique needs of the field. Therefore， this paper aims to discuss the methodology for customizing domain-specific foundation models， including the definition and types of foundation models， the description of their general architecture， the theoretical foundations behind the effectiveness of foundation models， and several feasible methods for constructing domain-specific foundation models. By presenting this content， we hope to provide guidance and reference for researchers and practitioners in the customization of domain-specific foundation models.