A fundamental task in the design of consumer products is consumer preference analysis. The primary focus of this task is establishing a mapping relationship between product parameters/attributes and consumer preferences. The key to connect the consumer space and the design space are user perceptions of the product. Among the many existing methods, the Structural Equation Model (SEM) is one of the most used methods because it explains the causal relationship between the input and the output variables explicitly. However, the relationship obtained from the conventional SEM is linear, which is usually not the case in practice. Fortunately, the Artificial Neural Network (ANN) provides a new perspective for building nonlinear models because of its nonlinear nature. Therefore, a two-phased SEM-NN approach for consumer preference analysis is introduced for identifying and mapping how product attributes affecting the fulfillment of user perceptions and ultimately their preferences. In this model, the consumer preference analysis is conducted in two phases: influence path construction, and path coefficient revision. The proposed method can reserve the original SEM topology that reflects the causal relationship between variables while using the training algorithm of ANN to obtain more accurate path coefficients. This model could help the designers to identify and map how product attributes affecting the consumer preferences, and to better understand the factors that affect user perceptions and the inner relationships between them. To demonstrate effectiveness of the model, a case study of smartphone is presented. It is shown that the SEM-NN model can make full use of the causal analysis of SEM and the nonlinear nature of ANN and ultimately provides more reliable results of consumer preference analysis.

消费产品设计中的一项基本任务是消费者偏好分析。该任务的主要重点是在产品参数/属性与消费者偏好之间建立映射关系。连接消费者空间和设计空间的关键是用户对产品的看法。在许多现有方法中，结构方程模型（SEM）是最常用的方法之一，因为它可以明确解释输入变量和输出变量之间的因果关系。然而，从常规SEM获得的关系是线性的，在实践中通常不是这种情况。幸运的是，由于其非线性特性，人工神经网络（ANN）为构建非线性模型提供了新的视角。因此，引入了用于消费者偏好分析的两阶段SEM-NN方法，用于识别和映射产品属性如何影响用户认知的实现，并最终影响他们的偏好。在此模型中，消费者偏好分析分两个阶段进行：影响路径构建和路径系数修正。该方法可以保留反映变量之间因果关系的原始SEM拓扑，同时使用ANN的训练算法来获得更准确的路径系数。该模型可以帮助设计人员识别和映射产品属性如何影响消费者的偏好，并更好地理解影响用户感知的因素以及它们之间的内部关系。为了证明该模型的有效性，本文以智能手机为例进行了研究。