Abstract: Intelligent decision-making is the core research direction in the field of intelligent ventilation in mines,and the prerequisite for achieving intelligent decision-making is effective processing,analysis,and representation learning of data. In response to the issues of low data utilization and insufficient knowledge reasoning capability in mine intelligent ventilation systems,this study constructs a knowledge ontology framework for mine ventilation based on the Coal Mine Safety Regulations(2022 Edition). It systematically evaluates the performance characteristics of knowledge representation learning models such as Trans E,TransR,RESCAL,DistMult,and Compl Ex,achieving vector representation and deep information mining of ventilation knowledge. By using loss functions and evaluation metrics,different dimensional knowledge representation methods were assessed,determining that the Trans E model has the optimal applicability in mine ventilation knowledge representation. Experimental results show that the Trans E model outperforms other models in terms of representation learning efficiency and accuracy,exhibiting a lower average loss value during the representation learning process,with similar ventilation entities showing significant clustering characteristics in the vector space,and optimal performance when the embedding vector is 40 dimensions.This model effectively represents ventilation knowledge,providing high-precision knowledge reasoning support for intelligent decision-making systems.