# Product Evaluation Prediction Model Based on Multi-Level Deep Feature Fusion
## 5 Conclusions  
* This paper raises a new DSCNN-BiLSTM prediction model which integrates attention mechanism with genetic algorithm optimization, and predicts customer satisfaction degree of a product from customer online comments, thus providing scientific decision-making for enterprise production design. Firstly, a shallow feature extraction model based on deep separable convolution is adopted to fully extract the associated features between product and evaluation satisfaction data from two dimensions, effectively solving the problem of insufficient feature extraction capability of classical convolution network. Secondly, the attention mechanism is embedded for secondary depth feature extraction, and the CBAM attention distribution feature weights are used to enhance the depth feature analysis capability of BiLSTM, which improves the self-learning capability of the algorithm and effectively solves the problem of important feature loss during long-term training. Finally, the number of layers and the number of neurons per layer of the depth-separable convolutional layers, BiLSTM layers and density layers are optimised using genetic algorithms, and the model parameters are retrained and optimised by calculating the fitness function values, so as to obtain the optimal network structure of the model. Compared with traditional machine learning algorithms such as SVR and DSCNN, BiLSTM has improved the performance of our model in predicting customer affective tendency index. The performance in MSE is especially surprising to us, which is as low as 0.001.
* Meanwhile, the deep learning network optimized by genetic algorithm has good self-adaptability, self-learning ability and generalization ability. The limitation of our model performance is that when predicting new data, the model parameters need to be adjusted by genetic algorithm, which can bring high prediction accuracy, but consumes a lot of time and computing resources.
* This article takes screens as research object to analyze the fluctuation of customer satisfaction caused by the change of some important product attributes (such as price and resolution, etc.), which provides reference for enterprises in product development and design. In the future work, we plan to use the model in this paper to calculate the emotional value of texts, perform cluster analysis on online comments, and refine customers' emotional tendency towards products from different perspectives, so as to replace the comprehensive evaluation of the emotional index in this paper. In addition, the optimization design of other adjustable parameters in the model is not limited to the structure of the model to further improve the performance of the model.  
Funding acquisition, Q.Z.; Investigation, Q.Z. and J.Z.; Methodology, H.L.; Project administration, Q.Z.; Resources, Q.Z.; Supervision, Q.Z.; Writing--original draft, Q.Z. and H.L.; Writing--review & editing, Y.Z. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program, grant numbers 2018 YFB1700702; the Natural Science Research Program of Anhui Agricultural University, grant numbers K2048004; the University Natural Science Research Program of Anhui Province, grant numbers KJ2021A0181; the Talent Research Program of Anhui Agricultural University, grant numbers rc482003.  
Not applicable.  
Data Availability Statement: Data available on request due to privacy restrictions. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the privacy implications of our data.  
The authors declare no conflict of interest.