# A Modeling Design Method for Complex Products Based on LSTM Neural Network and Kansei Engineering
## 1 Introduction  
Complex products (CPs), such as aircraft and automobiles, are products that involve complex structures and technologies and complex development, manufacturing and service processes [1, 2]. It is necessary to meet the needs of specific users, the diversity of product structure, and product design innovation during the design process [3]. Currently, research on CP design mainly focuses on the mechanical structure. For example, [PERSON] et al. applied complex network theory and proposed an improved GN algorithm (crowd detection algorithm) to achieve the module division of complex mechanical products [4]. [PERSON] et al. introduced a new CP optimization design framework based on three aspects, modeling, simulation, and optimization. This design framework can effectively determine the optimal design structure configuration and optimal functional parameter values of CPs [5]. [PERSON] et al. proposed a design optimization method for CPs, which canimprove the design optimization efficiency based on Multidisciplinary Design Optimization (MDO) technology [6]. Nevertheless, there are few studies on CP modeling design, which mainly focus on appearance design innovation. For example, [PERSON] et al. proposed a method for the appearance design of Numerical Control (NC) equipment based on product identification [7]. [PERSON] et al. proposed a novel method for the appearance design of mechanical and electrical products based on entity pattern genes [8]. However, the above methods failed to take affective factors into account in the design process of CPs and are not suitable for the affective design of CPs. As the economy evolves, customers pay more attention to affective experience than functional performance and usability when purchasing and using products [9]. Users' affective experiences are closely related to their perceptual needs for CPs [10, 11]. Compared with simple products, the design of CPs has a longer cycle [12] and a higher cost [13]. Moreover, it is difficult to effectively meet the perceptual needs of enterprises and users [14]. Therefore, it is a challenge for industrial designers to quickly and efficiently perform CP modeling design that meets the perceptual needs of enterprises and users. Kansei Engineering (KE) [15] has been widely used as a quantitative analysis method for affective design and new product development. KE has three core modules: acquisition of perceptual requirements, the establishment of the mapping models between modeling features and perceptual images, and design execution [16]. Among them, the mapping model between the product modeling features and the perceptual images is the key to affective designs [17]. Based on these mapping models, GAs [18], Tabu search (TS) algorithm [19], NSGA-II [20], and other methods can be carried out to quickly and effectively obtain the recommendation strategy of innovative modeling design that meet the perceptual needs of enterprises and users. In addition, the mapping models are also the basis for building an emotion preference computing and recommendation systems [21]. For example, [PERSON] et al. proposed a mapping models of Kansei knowledge and emotional color image words to help designers and consumers to obtain the most appropriate color ranges of consumer products [22]. [PERSON] et al. established a user-personalized clothing-recommendation system through the relationship model between emotional vocabulary and clothing elements to improve the recommendation accuracy [23]. [PERSON] et al. proposed a new fashion evaluation method on the basis of the appearance to build a clothing-recommendation system with higher accuracy [24]. Using online product click data and offline product sales data to reflect customers' online and offline preferences, [PERSON] et al. built a recommendation system to improve product sales and website click rates [25].