To address the issues of fragmented knowledge and limited information sharing in the field of workpiece machining distortion, this paper proposes a knowledge graph framework tailored for this domain. A dataset comprising 1 085 long-text records related to machining distortion was constructed, and an ontology model was developed to define the relevant entities and relationships. A BERT+BiLSTM+CRF-based entity extraction model and a BERT-based relation extraction model were employed to enable automated knowledge extraction. Knowledge fusion was performed through a combination of cosine similarity-based matching and manual verification. A knowledge graph containing 4 330 entities and 5 509 relationships was then built using the Neo4j graph database, with support for data insertion, deletion, updating, and querying implemented via Cypher language. Experimental results demonstrate that the proposed knowledge graph can effectively support decision-making and research analysis in workpiece machining processes.