## 4 Experimental Design
### Result Analysis  
#### 4.3.1 Classification Results of MLP Model Based on Different Feature Combination  
Firstly, the MLP classification model trained by the TF-IDF feature extraction method is taken as the baseline system. Secondly, the features of demand generation (DG), information collection (IC), product evaluation (PE), purchase decision (PD), and post-purchase behavior (PB) in consumer purchase decision making are trained separately, and the combined features of the whole consumer purchase decision making process (PDP) are trained to obtain multiple different MLP classifier models. The feasibility and effectiveness of identifying the usefulness of online reviews from the perspective of consumers' purchase decision-making process are verified by comparative analysis. In addition, in order to judge the necessity of each index in each stage and the degree of impact on the usefulness of online reviews, for the purchase stage with multiple indexes, it is necessary to identify the decline in model recognition results after removing a single index. That is, for the two stages of product evaluation and post-purchase behavior, the relevant six indicators are removed one by one, and the impact of each indicator on the usefulness of online reviews is judged by comparing the decline degree of the model identification results. The specific experimental results are shown in Table 2.  
Figure 5: Division of data. As shown in Table 2:  
1. As a baseline system, the effectiveness (F\({}_{1}\)) of the MLP neural network model trained by the TF-IDF feature extraction method to identify the usefulness of online reviews is 59.2%. However, the MLP neural network model proposed is to train the multi-stage combined features from the consumer purchase decision-making process. The recognition effect (F\({}_{1}\)) reached 88.9%, compared with an increase of nearly 30%. The results show that the feature extraction method, considering the purchasing decision-making process of consumers, significantly improves the classification effect of the MLP classifier. The usefulness of online reviews can be identified not only from the comments themselves but also from the whole purchasing decision-making process of consumers, and the usefulness of online reviews can be identified through the characteristic indicators in each stage;
2. Both the classification effect of the classification model with the indexes of each stage trained separately and the other features of the multi-index stage trained after the removal of a single index decrease to a certain extent. Among them, by analyzing the recognition effects of classifier models at different stages of consumer decision-making, it can be seen that the influence of each stage on the usefulness of online reviews, in descending order, is: post-purchase behavior, product evaluation, purchase decision, information collection, and demand generation. In the product evaluation stage, N\({}_{\text{PSP}}\) (the number of other products to be viewed in the store where the target is located) has a greater impact on the identification results than N\({}_{\text{PP}}\) (the number of similar products to be viewed). In the post-purchase behavior stage, the degree of influence on the recognition results of the usefulness of online comments, in descending order, is: N\({}_{\text{b}}\) (the number of comments to be viewed), N\({}_{\text{g}}\) (the number of likes on comments), D\({}_{\text{fa}}\) (the days between the first comment and additional comments), and D\({}_{\text{rc}}\) (the days between receiving goods and commenting). In order to further optimize the recognition effect of the classification model and study the necessity of each index, each refinement index is added to the feature representation in turn according to its influence degree, from large to small, on the classification results. The recognition effect of the nine classifiers obtained is shown in Table 3.