This article, from the perspective of structural features, focuses on in-car user interface icons and explores the impact of different icon structural features on visual search efficiency. Initially, we categorised the icons into four groups based on structural features: individual structure icons (ISI), enclosed structure icons (ESI), horizontal structure icons (HSI) and vertical structure icons (VSI). Subsequently, we conducted a visual search experiment with structure as the sole variable, recording participants' behaviours and eye-tracking data. Finally, data analysis was conducted using methods including analysis of variance and logistic regression. The results indicate that differences in icon structural features significantly affect visual search efficiency, showcasing significant intergroup differences. HSI exhibit the highest visual search efficiency, while ESI show the lowest efficiency. ISI have shorter response times but the lowest matching accuracy. VSI only perform better than ESI. These findings hold significant implications for optimising icon design and enhancing visual search efficiency.

In the era of emotional consumption, it has become a hot topic that commodities meet consumers’ emotional needs. As a necessity of life, the car also needs to meet the needs of consumers. To achieve that consumers can purchase cars according to their emotional needs, we need to label cars with emotional words. The car’s appearance is the crucial medium of emotional information transmission, especially the car’s color is an essential emotional factor. As the first impression of products, color affects people’s emotional attitude. Therefore, introducing color features into the training process of sample marking is an excellent idea for intelligent labeling of a large number of product emotions. This paper proposes a semi‐supervised learning method, Color‐SSL, based on color data augmentation to realize the label of car emotion. Color‐SSL takes FlexMatch as the framework of a semi‐supervised learning model and augments data by extracting subject color. Compared with the baseline method, the accuracy of this method improved by 3.2%, 8.3%, 8.6%, and 1.4% with 10, 50, 100, and 200 training samples and 1000 test samples. The results show that Color‐SSL obtains the best emotion‐label result (94%). In addition, this study publishes pictures of emotional car datasets with high resolution, orthogonal perspective, and uniform background.

Group preferences are crucial for Inspirational Solutions of Automotive Design (ISAD). However, sparse individual purchase behavior hinders the identification of group preferences. Therefore, a novel inspiration recommendation (IR) system based on multi-level mining of user behavior data is proposed. Firstly, the K-means algorithm is employed to cluster users based on a variety of features. The fixed association rule is then applied to filter and identify relevant subsets, forming the foundational basis for constructing a user portrait. The Nonlinear Bayesian Personalized Ranking (NBPR) is constructed to explore common preferences using explicit feedback. Finally, the item preference matrix is enriched with implicit feedback to compile a comprehensive recommendation list that caters to group preferences. Using a multi-user joint evaluation approach, we compare the performance of IR with baseline models across multiple metrics. This comparison demonstrates the robust reliability of the IR system and its ability to prioritize ISAD with preference-aligned groups. Our research overcomes data sparsity in the automotive recommendation system, providing a new method for embedding human elements in decision support systems.