[10] used a numerical simulation to analyze the local flow field characteristics of a louvered windshield in mine roadways at different opening angles, and derived the local resistance coefficient relation of a louvered windshield. [PERSON] et al. [11] established a 1:5 utility tunnel scale model and conducted model experiments under different conditions of the air volume, pipe diameter, and pipe layout to obtain the variation rule of the local resistance at the inlet and outlet of an integrated corridor ventilation system. A two-factor analysis of variance was used to analyze the significance of the influence of the Reynolds number, air volume, and the proportion of the pipeline area ratio. [PERSON] et al. [12] studied the law of influence of the guide vane on the local pressure loss of an air duct elbow through a model experiment and numerical simulation and carried out optimization research on an elbow guide vane. It can be seen from the above studies that numerical simulations and model experiments are the main methods used to study the ventilation local resistance. However, numerical simulations and model experiments have disadvantages such as a low calculation accuracy, low efficiency, and high cost of human and material resources, and numerical simulation methods also largely depend on grid division, the selection of the turbulence model, and governing equation-solving methods. Moreover, measurement and human operation errors are unavoidable in the model experiment. Therefore, there are many scholars in the field of fluid mechanics using machine-learning methods to carry out relevant research [13; 14; 15], using the black box model of machine learning to replace complex physical mechanisms in fluid mechanics, to overcome the shortcomings of numerical simulations and model-testing methods. [PERSON] et al. [16] used ridge regression, decision tree, random forest, gradient boosting regression tree, and other machine-learning methods to predict the average wind pressure and fluctuating wind pressure of high-rise buildings. [PERSON] et al. [17] conducted research on the surface wind pressure of low-rise buildings, obtained the surface wind pressure under different wind forces by using numerical simulation methods, established a surrogate model based on machine learning, and applied it to optimize the placement of building surface pressure sensors. [PERSON] et al. [18] used adaptive neural-fuzzy inference system, support vector machine, M5 model tree, least-squares support vector machine, and other intelligent prediction models to predict the overflow coefficient of curved pipelines, establishing the mapping relationship between the upstream water head, overflow ratio, curvature, and overflow coefficient. [PERSON] et al. [19] used machine-learning algorithms to predict dune movement patterns under different wind conditions, providing a technical reference for predicting sediment migration paths. [PERSON] et al. [20] used artificial neural networks to predict the pressure loss of crude oil transport pipelines, taking the pipe diameter, average flow rate, oil-water density, oil-water viscosity, and water content as the input parameters. In the field of mine roadways, some scholars have also carried out research on ventilation resistance coefficient prediction. [PERSON] et al. [21] established a BP neural network prediction model for the roadway ventilation resistance coefficient and applied the prediction results to the mine ventilation network model. Based on the least-square method, [PERSON] et al. [22] proposed a mathematical model to determine the ventilation resistance coefficient of mines using the inversion of the air volume and node pressure data, and adopted a genetic algorithm and particle swarm algorithm to solve the ventilation resistance coefficient inversion optimization problem.