<p>The debugging process scheme is pivotal in ensuring that electronic products meet the expected quality and performance standards prior to market launch. Its design process involves the meticulous management of diverse resources and process information. Traditional approaches typically rely on the manual extraction of design knowledge from large volumes of debugging data, a method that is not only inefficient but also hinders the effective reuse and management of knowledge. To address this, this paper proposes an intelligent design methodology for the debugging process of complex electronic products, grounded in knowledge graph techniques, aimed at expanding the design knowledge space and enhancing design efficiency. Initially, based on the structural composition of the debugging process scheme, this paper introduces a five-dimensional feature knowledge ontology to construct the debugging process design knowledge model, and leverages knowledge graphs to enable the structured representation and management of knowledge. Next, based on the debugging process units and process pathways, a knowledge retrieval algorithm is proposed to support the design of the debugging process. Subsequently, the debugging process knowledge is configured by training a Bayesian network model, which generates a structured tree of debugging solutions. These solutions are then optimized and customized to meet the specific requirements of the product. Finally, based on the aforementioned methodology, this paper develops a system tool to support the intelligent design of debugging processes for complex electronic products, which is validated through a case study on the design of a debugging process scheme for communication navigation and identification products. Experimental results demonstrate that the system effectively assists designers in reusing debugging design knowledge and shortening the design cycle.</p>