# Product Evaluation Prediction Model Based on Multi-Level Deep Feature Fusion
## 4 Experiment and Result Analysis
### Comparative Experiment  
SVR were used to conduct prediction experiments on the data in this paper, and the prediction results of each model were visualized (as shown in Figure 12). SVR is widely used to predict time series and multivariable prediction problems.  
In this section, the ablation experiment is carried out. DSCNN-BiLSTM and the model in this paper are combined with DSCNN and BiLSTM as the benchmark model, and Adam optimizer is used for training of all models. Adam optimizer parameters are set as follows: The learning rate = 0.001, \(\beta_{1}\) = 0.9, \(\beta_{2}\) = 0.009, the loss function is MSE, and the dropout is set to 0.3. The convolution kernel size of DSCNN, DSCNN-BiLSTM and the convolution layer of the model in this paper was set as 2, and RELU was used as the activation function to reduce the amount of calculation and prevent over-fitting. The above model each layer after adding batch of standardized BN, in the training process converts the distribution of each layer of neurons input values to the normal distribution, reduce internal covariance deviation, gradient bigger to avoid gradient disappeared, to accelerate the convergence speed, enhances the model generalization ability, the dropout technology in the whole connection layer to join L2 regularization operation, overfit  
Figure 11: Fitness graph under different fitness functions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fitness** & **MAE** & **MSE** & **RMSE** & **MAPE** & **SMAPE** \\ \hline MAE & 0.0322 & 0.0015 & 0.0389 & \%3.5460 & \%3.5496 \\ MSE & 0.0370 & 0.0018 & 0.0420 & \%4.0497 & \%4.0714 \\ RMSE & 0.0326 & 0.0015 & 0.0388 & \%3.5929 & \%3.5840 \\ MAPE & 0.0269 & 0.0010 & 0.0316 & \%2.9543 & \%2.9597 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model performance under different fitness functions.  
Figure 12 shows the predicted results of ablation experiments conducted with the model in BiLSTM, DSCNN and DSCNN-BiLSTM respectively. Number represents the product number in the validation data, represents a total of 20 displays, and Value represents the satisfaction Value of customer evaluation. It can be clearly seen from the figure that the prediction curve of machine learning SVR model deviates significantly from the actual value, and the absolute prediction error is obviously greater than 0, indicating poor prediction effect. However, DSCNN, BiLSTM benchmark model prediction model and its combination prediction model using deep learning have improved the prediction effect, but compared with ARIMA with accurate modeling, it is easy to fall into local extremes, leading to problems such as overfitting, which makes it difficult to achieve the best prediction results. In this paper, the attention module is embedded in the DSCNN-BILSTM and the model structure is globally optimised using genetic algorithms. Compared with the other five models, the prediction curve of this model is closer to the true value curve and the absolute error is closer to zero.  
The prediction results of the models in this paper were compared with those of the other models (as shown in Table 4). In the evaluation and prediction of 20 displays, the prediction effectiveness of this paper's model was improved to varying degrees compared to the other five models. Compared with the machine learning SVR model, the prediction effectiveness of this paper's model was reduced by about 3.35% and 3.29% for MAPE and SMAPE respectively. To some extent, it shows the efficient feature extraction ability of deep learning models to predict the prediction performance of the models.  
Figure 12: Graph of predicted results of different models.  
learning model. Compared with DSCNN and BiLSTM, the proposed model has improved in all evaluation indicators, and compared with DSCNN-BiLSTM deep learning network without adding attention mechanism and optimization algorithm, the proposed model has reduced about 2.34% and 2.71% in MAPE and SMAPE, respectively. The validity of the proposed method is further verified.