Flexible manufacturing significantly enhances production adaptability but imposes higher requirements on the design efficiency of automation control systems. Mining existing project information and transforming it into reusable function blocks is an effective approach to achieving efficient development of industrial control programs. However, the precision of manually generated function blocks depends on the individual understanding of the developer, which lacks stability and can easily cause misunderstandings in later use and maintenance. Therefore, we proposed a graph-based intelligent wrapping framework for industrial control program snippets to enhance the normalization of custom function blocks. The framework utilizes service computing and LLMs to intelligently assist in generating function blocks. The whole process includes program graph representation, semantic understanding, and automatic organization. Finally, we evaluated the proposed framework through comparative experiments in three practical projects that involved up to$\mathbf{1 0 0}$function blocks.