Abstract: Abstract: Once the port equipment malfunctions, it will seriously affect the efficiency of port production operations and cause safety hazards. Although a large number of scientific research literatures on equipment faults of port are gathered, they often have inconsistent expressions due to different focuses, which increases the difficulty for researchers and managers to timely grasp the current research status and quickly acquire knowledge. Constructing knowledge graph for port equipment faults helps to conduct knowledge queries and intelligent Q&A on port equipment faults, enhancing the efficient management and dissemination of knowledge on port equipment failures. To address this, this study adopts titles, abstracts, and keywords from Chinese literature on port equipment failures as dataset text and then employs the BIO data annotation method and the BERT-BiLSTM-CRF model to extract information about port equipment failures from the text. By introducing ontologies to formally describe entities and their relationships for solving inconsistencies and forming triplets, a knowledge graph of port equipment failures is constructed. This graph supports knowledge queries and intelligent Q&A related to port equipment failures. The construction of a crane failure knowledge graph serves as a case, validating the effectiveness of the knowledge graph construction, thereby enhancing the efficient management and dissemination of knowledge about port equipment failures.

Key words: Key words: port equipment malfunction, knowledge graph, text mining, named entity recognition, intelligent Q&A