A product is composed of several components, and the number, type, and combination of components plays a crucial role in the process of product design. It is difficult to get an optimized scheme in a short time. In order to improve the efficiency of product design, a product design scheme recommendation algorithm based on multi-way tree and learning-to-rank is proposed. Firstly, the product solution model, whose nodes are obtained by mapping the product attributes, is generated according to the design process, and the alternative scheme is obtained by traversing the multi-tree model. Secondly, considering users’ cognition of the importance of each product attribute, the analytic hierarchy process (AHP) is applied to assign weight to the product attribute, and then similarity to ideal solution (TOPSIS) method based on AHP is used to rank alternative solutions. Furthermore, according to users’ preference for parts’ supplier information, the learning-to-rank algorithm is used to optimize the list of alternative schemes twice. Finally, taking the design of the hoist as an example, it was verified that the proposed method had higher efficiency and better recommendation effect than the traditional parametric design method.
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