###### Abstract  
In the context of the continuous development of e-commerce platforms and consumer shopping patterns, online reviews of goods are increasing. At the same time, its commercial value is self-evident, and many merchants and consumers manipulate online reviews for profit purposes. Therefore, a method based on Grounded theory and Multi-Layer Perceptron (MLP) neural network is proposed to identify the usefulness of online reviews. Firstly, the Grounded theory is used to collect and analyze the product purchasing experiences of 35 consumers, and the characteristics of the usefulness of online reviews in each stage of purchase decision-making are extracted. Secondly, the MLP neural network classifier is used to identify the usefulness of online reviews. Finally, relevant comments are captured as the subject and compared with the traditional classifier algorithm to verify the effectiveness of the proposed method. The experimental results show that the feature extraction method considering consumers' purchase decisions can improve the classification effect to a certain extent and provide some guidance and suggestions for enterprises in the practice of operating online stores.  
online review; the usefulness of online reviews; multilayer perceptron neural network; Grounded theory +
Footnote †: journal: Medical  
-employ professional \"brushes\" to carry out fake shopping behavior for the purpose of increasing sales. The purpose of merchants is only to increase the sales of commodity links and the praise written by \"brushes\" after confirming receipt of goods. In the process of fake shopping, it is difficult for \"brushes\" to operate according to the shopping process of real consumers. Moreover, due to the lack of experience and feelings of real consumers about the goods, the online comments written by them cannot truly reflect the real attributes and shopping experience of the goods. Therefore, the identification of online comments should start not only from the comment text itself but also from the whole process of consumer purchase to identify online comments that do not conform to the real purchase behavior of the order.  
In summary, a method is proposed to identify the usefulness of online reviews by using Grounded theory and an MLP neural network to consider consumer purchasing behavior. Firstly, based on the Grounded theory, a semi-structured interview is conducted on the real shopping experiences of 35 interviewees, and the usefulness of online reviews is characterized by open coding, spindle coding, and selective coding from each stage of consumers' purchase decision making. Secondly, the captured data are used to test and train the model. Through multiple sets of horizontal and longitudinal experiments, the optimal feature combination that best matches the MLP neural network classifier model is found and compared with other traditional classifiers so as to verify the superiority of the classification effect of the model.  
The remainder of the paper is organized as follows: Section 2 reviews the literature on the influencing factors and recognition algorithms of the usefulness of online reviews. Section 3 introduces the process of identifying the usefulness of online reviews' considering consumers' purchase decisions based on Grounded theory. Section 4 carries out the experimental design and verifies the validity and interpretability of the proposed method through multiple sets of horizontal and longitudinal comparison tests. Section 5 gives the experimental conclusion and briefly introduces the shortcomings and future prospects of the paper.