In the context of rapid product iteration, design conflicts arise from discrepancies in designers’ understanding of user needs, influenced by subjective preferences, behavioural stances, and other factors. This paper proposes a product conceptual design approach based on the design conflict perspective. First, user comments and design documents are collected. Natural language processing (NLP) methods, including cleaning, filtering, lexical segmentation, feature clustering, and sentiment analysis, are employed to identify design themes. The intuitionistic fuzzy sets (IFSs) and term frequency–inverse document frequency (TF-IDF) algorithms are then utilized to obtain evaluation matrices for the products from both users and designers. Subsequently, design conflicts between users and designers are calculated, and an optimal configuration for product conceptual design is determined through regression analysis and planning methods. Finally, the proposed method is validated using a mobile phone as a product example, and suggestions for product improvement are presented. The results indicate that considering design conflicts as a factor in product design and synthesizing designer and user product concepts enhance the accuracy and reliability of product conceptual design generation. The findings of this study offer new insights into the conceptual design configuration for product iteration.

In order to improve the organization and management ability of R&D resources in the group enterprise, and realize the efficient utilization of R&D resources, this paper puts forward the design resource space automatic construction classification method based on the analysis of the space construction requirements of R&D resources in the group enterprise. Machine learning is used to extract keywords from R&D resources, and semantic maps are built based on the semantic similarity of keywords. Hierarchical clustering is implemented through a community detection algorithm. Then, based on the BRET-LEAM model, the mapping of R&D resources to resource space is achieved, and the construction of multidimensional R&D resource space automatic classification is finally completed. Finally, an instance of group enterprise R&D resources automatic classification is built.