Clinical practice guidelines contain substantial diagnostic and therapeutic process knowledge to guide clinical decisions. However, since this knowledge is often expressed in natural language, it is difficult for computers to directly understand and execute, which limits its intelligent application. To address this issue, this study proposes a systematic framework to achieve the computability of guideline process knowledge. The framework covers the full technical path from process concept standardization, ontology modeling, sentence recognition, to process visualization. The study first clarifies the standardized definition of diagnostic and therapeutic processes and their core elements, constructing a unified semantic framework. Next, based on ontology engineering methods and business process model and notation process models, a computable and inferable diagnostic process ontology was built. Then, combining manually annotated corpus with large language model prompt engineering strategies, high-precision automatic recognition and key element extraction of diagnostic process sentences were achieved. Finally, process modeling and visualization techniques were used to transform the extracted knowledge into structured graphical representations, providing intelligent knowledge graph support for clinical decision support systems. This study provides a reproducible technical foundation for the structured expression and intelligent application of clinical practice guideline diagnostic and therapeutic process knowledge.

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