Although artificial neural networks have been shown to have a superior performance, they are prone to poor learning and generalization performance, and also suffer from drawbacks such as the network structure not being easily determined and easily falling into local minima [20]. Support vector machine regression (SVR) is a model based on statistical theory that can better solve the regression problems and has been widely used in prediction problems in different fields [21]. [PERSON] et al. [22] proposed a comprehensive evaluation prediction model for Chinese passenger car pedals based on support vector machine regression that reasonably designed the pedal operation criteria, limits, and recommended ranges by evaluating the relevant pedal parameters and used a Back Propagation (BP) neural network for comparison and validation. The results showed that the effectiveness and reliability of support vector machine regression in predicting pedal design and performance prediction were better than those of a neural network. [PERSON] et al. [23] established a BP neural network model and a support vector machine regression model to evaluate the sound quality of each operating state and position of tractors, used a genetic algorithm (GA) to optimize the relevant parameters of SVR, and finally concluded that the GA-SVR model predicted the sound quality with higher accuracy and stability. [PERSON] et al. [24] used the simulated annealing algorithm to find parameters for the support vector machine regression prediction model, and based on finite element simulation, the optimal parameters and the predicted value of the optimal internal helical groove cutting force were derived, which led to an improvement in the accuracy of the SVR with a good prediction effect. Particle swarm algorithm (PSO) has the advantages of a fast convergence speed, high efficiency, etc. However, with more in-depth research, problems such as PSO's own slow convergence speed are gradually revealed, which affects the prediction accuracy. Therefore, many scholars have proposed optimizing the PSO in order to improve all kinds of research. [PERSON] [25] and others proposed an improved chaotic binary particle swarm optimization algorithm (MCBPSO) for feature selection in response to the low recognition rate of unimodal biometric features, and the classification accuracy of the kernel-extreme learning machine (KELM) was calculated as the fitness value of the particles, which was verified through experiments to improve the recognition rate. He et al. [26] combined the genetic algorithm and particle swarm algorithm to perform an adaptive search of spatial solutions in the spatial range by taking the minimum annual cost of substation planning as the fitness, effectively avoiding the local optimal solution and premature maturity problem and verifying the hybrid algorithm's more optimal searching ability and convergence characteristics through examples. In summary, most of the research on comfort conducted by scholars at home and abroad has focused on judging the comfort of vehicles and office seats, with less research having been conducted in the field of aviation. With the increase in air traffic, the study of the comfort of seats, as objects that people directly contact, is particularly important. In this paper, the authors propose using SVR to predict the comfort of aircraft drivers' seats, to explore the characteristics of complexity and nonlinearity between subjective and objective evaluations. SVR has a better ability to generalize and a higher level of accuracy when fitting data and making predictions. It also avoids the problems of local minima, over-learning, and dependence on the number of samples that exist in neural networks and other methods [27]. In this paper, a linearly decreasing dynamic inertia weight value is used to improve the traditional particle swarm algorithm.