Intelligent Product Concept Design Method Based on Semantics of Competing E‐Commerce Products

To address the limitations of existing product concept design (PCD) methods in the rapidly changing market environments, this study proposes a PCD method using e‐commerce product data and artificial intelligence techniques. First, data of competing e‐commerce products are acquired from an e‐commerce platform. Second, monthly sales of products are categorized and selected as the indicator for evaluating product concepts (PCs). Third, Doc2Vec is used to vectorize the product description to obtain the semantic representation of PCs, and a machine learning‐based PC evaluation model is built using the concept vector as features. Finally, a PC element library is built based on Word2Vec, and the tabu search algorithm is applied to identify the optimal combination of concept elements, determining the most favorable combination of PCs for the new product. Results indicate that the PC evaluation model based on multilayer perceptron achieves an average accuracy of 85.62% in predicting the quartiles of sales in the case of middle‐aged and elderly home products, with the area under the receiver operating characteristic curve ranging from 0.96 to 0.99. The proposed PCD method can produce novel PCs with good market potential and a high degree of automation, improving the time efficiency and quality of PCD.