With the development of intelligence and network connectivity, the development of the automotive industry is also moving toward intelligent systems. For passengers, the utility of intelligence is to achieve more convenience and comfort. The intelligent cockpit is the place where passengers directly interact with the car, which directly affects the experience of passengers in the car. For the intelligent cockpits that have emerged in recent years, a reasonable and accurate comfort evaluation model is urgently needed. Therefore, in this article, from the passenger’s perspective, a subjective evaluation experiment was set up to collect data on four important indicators affecting the comfort of the intelligent cockpit: sound, light, heat, and human–computer interaction. The subjective evaluation weights were derived from a questionnaire, and the entropy weighting method was used to obtain the objective weights. Finally, the two weights were combined using the idea of game theory combination assignment to get the final accurate weights. Using the idea of penalty type substitution, the four index models were then synthesized to get the final evaluation model. The feasibility of the model was verified when measuring the car cockpit. The feasibility of the method means it can evaluate the comfort level of an intelligent cockpit more reasonably, facilitate the enhancement and improvement of the model, and promote the development of the model to achieve maximum passenger comfort.

The entropy method is used in many decisions on indicator weights and has been proven to be effective. The traditional entropy method has two shortcomings in calculating indicator weights. On the one hand, when the developed indicator is considered to have no influence, it will lead to the meaningless calculation formula of the entropy method, and on the other hand, when the entropy method is dealing with data with entropy values close to 1, there will be entropy jumps leading to data distortion. To solve the above shortcomings, two correction coefficients are introduced in this paper, and the improved entropy weight method is applied to the evaluation model of the passenger comfort of the intelligent cockpit of the car, and the effectiveness of the improved entropy weight method is proved.