The Frontiers of Society, Science and Technology, 2025, 7(1); doi: 10.25236/FSST.2025.070103.
Xiaoxu Wang, Jian Ding
College of Industrial Design, Luxun Academy of Fine Arts, Shenyang, Liaoning, 110004, China
With the increasing demand of consumers for automotive interior design, traditional user experience evaluation methods are no longer able to meet the personalized and refined needs. This article proposes a neural network-based user experience optimization method for automotive interiors, aiming to analyze user needs, preferences, and experiences through deep learning techniques to optimize interior design. By constructing a neural network model and utilizing a large amount of user feedback data, the study accurately captures complex user perceptions and needs, surpassing the limitations of traditional questionnaire surveys and expert evaluation methods. The experimental results show that neural network models have significant advantages in predicting user preferences, improving design efficiency, and optimizing user experience. They can provide personalized design solutions and optimization suggestions for design teams, further enhancing user satisfaction and brand loyalty. This study provides theoretical support and practical guidance for the intelligent and personalized development in the field of automotive design, and has important application value and prospects.
neural network, automotive interior, user experience, design optimization
Xiaoxu Wang, Jian Ding. Research on User Experience Optimization of Automotive Interior Based on Neural Networks. The Frontiers of Society, Science and Technology (2025), Vol. 7, Issue 1: 14-21. https://doi.org/10.25236/FSST.2025.070103.
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