Acquisition Method of User Requirements for Complex Products Based on Data Mining

Abstract

The vigorous development of big data technology has changed the traditional user requirement acquisition mode of the manufacturing industry. Based on data mining, manufacturing enterprises have the innovation ability to respond quickly to market changes and user requirements. However, in the stage of complex product innovation design, a large amount of design data has not been effectively used, and there are some problems of low efficiency and lack of objectivity of user survey. Therefore, this paper proposes an acquisition method of user requirements based on patent data mining. By constructing a patent data knowledge base, this method combines the Latent Dirichlet Allocation topic model and a K-means algorithm to cluster patent text data to realize the mining of key functional requirements of products. Then, the importance of demand is determined by rough set theory, and the rationality of demand is verified by user importance performance analysis. In this paper, the proposed method is explained and verified by mining the machine tool patent data in CNKI. The results show that this method can effectively improve the efficiency and accuracy of user requirements acquisition, expand the innovative design approach of existing machine tool products, and be applied to other complex product fields with strong versatility.