Abstract: The research is aimed at improving the accuracy of product modeling image formation and enhancing the standardization of image vocabulary extraction and knowledge reusability in the research on perceptual engineering. This research started with the concepts of homogenousness, pan-ethnicity, and heterogeneousness, and defined the model image ontology of the target product family; then, with the help of word2vec tools and principal component analysis (PCA), the relevance association and dimensionality reduction extraction of the product family image vocabulary was respectively completed, thereby constructing an image vocabulary mining mechanism. This mechanism can assist designers to more efficiently and accurately mine the image of the target product using network big data resources, which can to a certain extent solve the vagueness of the image mining method in traditional perceptual engineering. Finally, combining the mapping relationship between the image vocabulary and the modeling features, the Protege tool was employed to construct the ontology model of the product family modeling image, which logically characterized the modeling image knowledge of the target product, and provided reference for the next-generation products to inherit and develop the original product family modeling image. Taking the modeling image of Mazda MX-5 car series as an example, the ontology model was constructed, and the conceptual design of the front face of the child product was undertaken. 

Key words: product family, ontology model, image mining, Word2vec, principal component analysis