Abstract: To effectively manage and utilize the vast amount of ventilation data in coal mines and meet the national demand for intelligent mining systems, the development of knowledge graphs and intelligent question-answering (Q&A) systems is a critical step in the intelligent transformation of mine ventilation. Using techniques like web scraping and classification analysis, knowledge data from the mine ventilation domain is collected and integrated with expert input to build an ontology model. This model serves as the foundation for constructing the mine ventilation knowledge graph, with 6 935 entity annotations and vertical relationship associations completed. Based on this knowledge graph, a vector-based intelligent Q&A framework was designed. Through the creation of question intent identification rules and answer templates, a vector-based Q&A model was developed. To verify its applicability, 200 professional questions related to mine ventilation were tested. The results show an overall accuracy of 95% for the model, with 97% accuracy for single-turn questions and 93% for multi-turn continuous questions. Compared to rule-based models, the vector-based model demonstrates significant advantages in multi-turn interactions. Future research will further improve the accuracy by integrating large language models for enhanced semantic analysis. This intelligent Q&A system will reduce the workload of ventilation personnel, increase management efficiency, and provide vital support for the full implementation of intelligent ventilation systems in coal mines.