The main idea of the application of machine learning in the field of fluid mechanics is to establish a high-dimensional mapping relationship between the input parameters and output targets through machine-learning algorithms, replacing the complex physical mechanism of fluid mechanics. The most critical part is obtaining the training sample set. In order to ensure the accuracy and efficiency of the prediction, accurate predictions of the ventilation local resistance coefficient should be obtained by using as few numerical simulation or model test results as possible. In addition, the ventilation local resistance coefficient is related to complex turbulent motion in the local area and has strong nonlinear fluctuation characteristics due to the comprehensive influence of both structural and ventilation parameters. Therefore, the prediction of the ventilation local resistance coefficient is a typical small sample nonlinear prediction problem. Relevance vector machine (RVM) is a sparse kernel method based on the Bayesian framework proposed by [PERSON] [23]. Its structure is similar to that of support vector machine (SVM), but its training speed is faster, and it has a strong advantage in nonlinear small sample prediction problems. Therefore, the RVM model has been widely used in engineering fields such as mechanical service-life prediction [24], slope deformation probability prediction [25], short-term power coincidence prediction [26], industrial fault classification [27], and pollutant concentration prediction [28]. However, a single-kernel RVM model cannot accurately excavate the deep nonlinear fluctuation characteristics of engineering data. Therefore, the hybrid kernel function is introduced into the RVM model to balance the global generalization ability and local learning ability and improve the prediction accuracy and generalization performance of the model. At the same time, the choice of kernel parameters for the HKRVM model will affect the prediction accuracy of the model. Scholars use swarm intelligent optimization algorithms such as the grey wolf optimization algorithm [29], grasshopper optimization algorithm [30; 31], particle swarm optimization algorithm [32], whale optimization algorithm [33], and bat optimization algorithm [34] to optimize the kernel function of the HKRVM model. The artificial jellyfish search algorithm (AJS) [35], as a swarm intelligent optimization algorithm proposed in recent years, has fewer adjustment parameters and a simple search process. It has been successfully applied in the fields of power grid energy scheduling [36; 37], medical image segmentation [38], civil structure engineering [39], and construction engineering image recognition [40]. The application results show that the AJS algorithm has a better optimization accuracy and optimization efficiency than other swarm intelligent optimization algorithms, but the AJS algorithm suffers from premature convergence and can easily fall into local optimal problems when solving high-dimensional nonlinear optimization problems. In summary, the local resistance coefficient of water transmission tunnel maintenance ventilation is one of the key parameters to be considered in the design of ventilation schemes, and it is related to the complex turbulent movement of fluid. In order to achieve the efficient and accurate prediction of the ventilation local resistance coefficient, this paper proposes a hybrid prediction model for the local resistance coefficient of water transmission tunnel maintenance ventilation, and establishes a mapping relationship between the structural parameters, ventilation parameters, and ventilation local resistance coefficient, in place of a complex fluid mechanics mechanism. It also provides a theoretical basis and technical reference for the optimization of long-distance water transmission tunnel maintenance ventilation schemes. The remainder of this paper is organized as follows: In Section 2, the research framework of this paper is put forward.