Abstract: Existing mine ventilation network solution methods do not consider the distribution of error weights in the ventilation network during the solution process, which leads to deviations in the airflow of each branch and limits the accuracy of the results. To address this issue, a ventilation network solution method integrating rough set attribute reduction and Weighted Least Squares (WLS) method was proposed. Based on rough set theory, a forward greedy algorithm was used to construct equivalence classes and calculate dependency degree, thereby achieving attribute reduction, and the key attributes highly correlated with the airflow distribution were identified as airflow, resistance, and cross-sectional area. According to the weights of the key attributes, Prim’s algorithm with a priority queue was employed to construct the minimum spanning tree of the ventilation network, and the co-tree branch set was further obtained. The WLS co-tree branch determination method was introduced to iteratively optimize the weights of each branch, reduce computational errors, and improve prediction accuracy. The results of engineering application analysis showed that, after introducing rough set attribute reduction, the solution time of the network was effectively reduced, and the efficiency and accuracy of ventilation network solution were improved. The WLS co-tree branch determination method controlled the airflow calculation deviations of 16 key co-tree branches within 0.1%–2.0%, demonstrating higher computational accuracy and stability.