Intelligent process design is the core of process design in the digital twin environment, and part process knowledge modeling is a prerequisite for achieving intelligent process design based on digital twins. To address the issues of low structured and difficult to reuse machining process data for complex thin-walled parts in the aerospace field, a onstruction and quality evaluation method for a typical knowledge graph of machining process for complex thin-walled parts is proposed. Firstly, the composition and structure of machining process knowledge are analyzed. Secondly, the visualization of process knowledge was realized through ontology modeling, knowledge extraction, knowledge storage, and other related technologies, and the knowledge retrieval of machining process was realized based on Neo4j database. Finally, the analytic hierarchy process is used to evaluate the constructed knowledge map, and the machining process knowledge of frame and segment parts is taken as the verification object, the comprehensive accuracy of the sub-map is 92.28%. The experimental results show that the process knowledge modeling method based on knowledge map is feasible, which can help to realize the effective organization and reuse of process knowledge, and lay the foundation for digital twin intelligent process design.