# A Modeling Design Method for Complex Products Based on LSTM Neural Network and Kansei Engineering
## 1 Introduction
The above studies inspire us by building relationship mapping models between user emotional preferences and design elements, and user emotion-aware recommender systems can be established to estimate user emotional preferences, recommend satisfactory products to users, and provide product development strategies that meet the users' preference to designers. There are two different types of KE methods applied to established the mapping models. The first one includes methods based on statistical theory, such as polynomial regression (PR), support vector (SVR), etc. For example, [PERSON] et al. evaluated the product perceptual value through PR [26]. [PERSON] et al. applied SVR to establish the mapping models between user emotion and car contour and achieved good results in a specific dimension [27]. This type of method has good interoperability but still has the following shortcomings: poor model performance and low generalization when dealing with complex multi-dimensional features and ignoring the multi-dimensional variables and potential nonlinear relationships in product perceptual images [28]. Therefore, they were difficult to use to describe the multivariate mapping relationship between CP perceptual images and modeling parameters accurately. The second type includes methods based on artificial intelligence (AI). By analyzing the features and structures of data, excavating the hidden information of data, and preserving the correlation among data, deep learning based on artificial neural networks can effectively solve the nonlinear problems among variables and deal with high-dimensional variables. For example, [PERSON] et al. proposed an affective design method for a multi-dimensional variable based on the BP neural network, which can effectively grapple with the mapping relationship between short-sequence features and perceptual images [28]. [PERSON] et al. constructed a Convolutional Neural Networks (CNN) neural network to establish the relationship mapping model between modeling features and perceptual images and achieve ideal results in processing a small number of feature data [29]. The above neural network can effectively establish the relationship model between perceptual images and the modeling features of simple products. However, CPs have complicated modeling features and a wide range of visual-influence factors [30], which are difficult to transform into deep learning data comprehensively and accurately. The methods above cannot recognize the affective preference of CPs from an overall visual perception level like human beings [31]. Therefore, establishing a mapping model between perceptual images and modeling features of CPs has become a challenge for designers. A Long Short-Term Memory (LSTM) network is a variant of the cyclic neural network [32]. The connection among units of LSTM forms a directed cycle. The introduction of a gate structure enables the network to capture the long-term dependence and nonlinear modeling parameter characteristics between timing data points, which makes it excellent in processing timing characteristics [33]. Since users generate an overall perceptual cognition of CPs through continuous visual perception, it is crucial for computers to learn how to observe the continuous feature relationship of products like human eyes and establish an accurate correlation model between perceptual images and modeling features. To address the question mentioned above, regarding the acquisition of perceptual evaluation as a sequence problem based on the LSTM neural network and eye movement test, a modeling design method was proposed to establish a more comprehensive and effective relationship model between perceptual images and modeling features of CPs. This paper presents a proposed method that can meet users' perceptual images more compressively and effectively based on the LSTM neural network and KE for the rapid modeling design of CPs (CP-KEDL).