A new sound quality prediction model for vehicle interior noise, based on the physiological structure of the human ear, is proposed to address the limitations of existing models that fail to effectively analyze differences in interior noise perception among individuals with varying hearing states. The model begins with the collection of interior noise samples from three cars, followed by subjective evaluation experiments to obtain subjective noise ratings. An auditory peripheral module is then constructed, incorporating an outer ear filter model, a middle ear lumped parameter model, a cochlear lumped parameter model, and a hair cell ciliary fluid coupling model to simulate the physiological structure of the human ear. An imitative auditory center module, designed to generate physiological loudness, sharpness, and roughness, is built using the Leakage Integral-and-Fire neuron model to simulate auditory nerve excitation, and deep neural networks to replicate the auditory center’s sound perception. The sound quality decision module is developed by integrating the psychoacoustic parameters generated by the auditory center module into a TabNet model to predict sound quality. Together, the auditory peripheral module, the imitative auditory center module, and the sound quality decision module form the complete sound quality prediction model. Finally, the model′s predictions are compared with those of existing models. Experimental results demonstrate that the proposed model accurately predicts vehicle interior noise quality, with an average prediction error of just 3.3%, outperforming the 6.4% error of artificial neural network-based models and the 7.7% error using the Zwicker model for psychoacoustic parameter calculation. This model offers a novel approach for studying the sound quality of in-vehicle noise for individuals with varying hearing states.