# Product Evaluation Prediction Model Based on Multi-Level Deep Feature Fusion
## 1 Introduction  
With the development of mobile Internet and e-commerce, after customers buy products online and experience them, they will generally write down their experience of using the products on the platform. Research on how to efficiently mine the emotional preferences of consumers to publish online reviews can provide consumers with scientific purchasing decision guidance, provide enterprises with targeted production design methods, improve customer experience and increase potential consumer groups [1]. However, in the face of such a large amount of comment data, it is not possible to control the market's diversified needs for products only from the perspective of enterprises. In Quality Function Deployment (QFD), The House of Quality (HOQ) is used to identify the link between customer requirements and the performance of the corresponding product or service to help product designers determine the optimal design attribute settings [2]. However, due to the need for a large number of questionnaires, the formulation of the HOQ is a long process, and after a long period of processing, the customer-oriented product design cannot be carried outin a timely and accurate manner. The new method is to extract key words and calculate the similarity of words in online reviews, parameterize the product image on the research object, construct the mapping relationship between product feature parameters and image parameters using BP (Back Propagation) neural network to form the product review model, and guide the design through the evaluation results [3]. The authors in [4] proposed a method. First, the semi-supervised learning recursive autoencoder (ss-rae) model is used to classify the comments of electric water heaters on the sentiment tendency, and the customer satisfaction is obtained according to the sentiment classification results, predict customer satisfaction over the next unit time. Determining the design attributes of new products is crucial to maximizing customer satisfaction. This research [5] proposed a data-driven product optimization design method, using the K-means algorithm to perform cluster analysis on rice cooker reviews, and optimize according to the clustering results. After encoding the target features, the Non-inferior Sorting Genetic Algorithm II (NSGA-II) was used to optimize the product features, and a rice cooker oriented to customer preferences was designed. This research [6] proposed a genetic algorithm constrained by bidirectional association rules to predict customer satisfaction and provide product design suggestions for enterprises. To sum up, the traditional product design plan that considers customer preferences uses the SD (Semantic Differential) questionnaire method to collect data, and then establishes a model through experience to obtain the design plan, which is time-consuming and labor-intensive. Recent research mainly uses online reviews to quantify product imagery, and establishes a model based on the method of Kansei Engineering to analyze the mapping relationship between product experience information and product design elements, so as to carry out customer-oriented product optimization design [7; 8; 9]. For the complex relationship between design elements and customer satisfaction, the existing research is not accurate enough to predict customer evaluation satisfaction, and a more efficient model is needed to predict customer evaluation satisfaction from the perspective of multiple design elements [10; 11], such as product color, The impact of quality and performance on customer satisfaction, and then provide more scientific decision-making for production design. Therefore, this paper proposes a multi-level deep feature fusion product evaluation prediction model, which has the following salient features in product evaluation prediction:  
* The model uses the fine-grained emotional quantification of various evaluations of consumers' online shopping products, and obtains the emotional value sequence data of the comprehensive customer experience.