为解决复杂矿井通风网络中存在的风量分配不合理、通风调节设施设置不当，以及通风功耗高等问题，基于图论与通风网络理论，构建以矿井通风网络风量分配基本定律、通风巷道风量风压、主要通风机运行工况为约束条件，以矿井通风网络运行功耗最低为目标函数的矿井通风网络非线性优化数学模型，并运用罚函数法将约束条件转换为目标函数的惩罚项。在标准鲸鱼群优化算法的基础上，提出一种融合混沌反向学习、Beta分布、非线性自适应惯性权重的多策略改进方法，通过提高算法的求解精度与收敛速度，增强算法的局部开发与全局寻优能力。采用改进鲸鱼群(IWOA)算法对模型进行求解，并将冀中能源股份有限公司邢东煤矿通风系统作为研究对象进行模拟，结果表明：该煤矿的矿井通风网络运行总功耗下降13.43%，矿井各用风巷道风量分布合理，符合矿井实际通风需求，证明所采用算法的可行性与优异性。

To address the defects of unreasonable distribution of air quantity, improper placement of ventilation control device, and high ventilation power consumption in complex mine ventilation networks, a non-linear optimization mathematical model for mine ventilation networks is constructed based on the graph theory and the ventilation network theory. This model takes the basic patterns of distribution of air quantity in mine ventilation networks, air quantity and air pressure in ventilation roadway, as well as the operating conditions of main ventilation fans as constraints, and uses an objective function that minimizes the ventilation network's power consumption. Afterward, the constraints are transformed into penalty terms in the objective function using the penalty function method. Based on the standard whale swarm optimization algorithm, a multi-strategy improvement method that integrates chaos reverse learning, Beta distribution, and non-linear self-adaptive inertia weight is proposed, with the view to enhancing the algorithm's local search and global optimization capabilities by improving solution accuracy and convergence speed. The improved whale swarm algorithm was used to solve the model, and the ventilation system of Xingdong Coal Mine of Jizhong Energy Co., Ltd. was simulated as the research object. The results show that the total power consumption of the mine ventilation network is reduced by 16.58%, and the distribution of air quantity of each air roadway in the mine is reasonable, which meets the actual ventilation requirements of the mine, demonstrating the feasibility and superiority of the adopted algorithm.