Personalized Art Design of Wheel Rims Based on Image Mapping of Image Requirements

In the customization of wheel rims, to convert users' emotional images and needs into design solutions, research is conducted based on pixel theory, using clustering algorithms, principal component analysis and other technologies to establish image association sample libraries, obtain image mapping relationships, and construct a wheel rim shape design platform system and system design improvements. The results showed that unlike methods such as support vector machines, the K-means algorithm had higher classification accuracy and smaller average absolute error. The classification accuracy of the K-means algorithm was 93.15%, and the support vector machine was 84.33%. The minimum average absolute error of the K-means algorithm was 0.56. In the application of the wheel personalized customization platform system, the improved design improved user satisfaction and ease of use, with corresponding scores of 4.40 and 4.35, respectively. The research method can transform user image needs into wheel shape design schemes to meet user needs.