With the rapid development of the e-commerce industry, online reviews of goods are a great help for consumers to make decisions. With the sharp increase in online order for goods and the explosion of product reviews, some merchants began to hire consumers to make fake purchases for profit, which led to the problem of identifying fake reviews. In this paper, we propose a method that uses feature engineering to eliminate the comments of false reviewers and combines convolutional neural network and recurrent neural network to classify and recognise reviews from the perspective of text. Traditional neural network models such as CNN, LSTM and BILSTM are compared with the hybrid model proposed by the text. The model is optimised by pre-training on the Baidu Baike commodity review database instead of the initial randomising word vector. The experimental results show that the combination of convolutional neural network and recurrent neural network can better extract the global and local features of false comments, and the model has a good effect. The updating of the pre-trained word vector makes the recognition effect of each model better.

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.

In the context of the explosive growth of online reviews in e-commerce, some merchants began to hire consumers to make fake purchases to increase sales, which caused the difficulty in identifying fake reviews. Therefore, from the perspective of consumers’ shopping behavior, a feature recognition method based on comment text, reviewer attribute and reviewer’s shopping behavior (referred to as TAB) is proposed to solve the problem of fake reviews caused by fake shopping behavior. Compared with BOW, TF-IDF, N-gram, FSP and other traditional feature extraction methods, experiments were carried out on Naive Bayes, SVM, Logistic Regression, Random Forest, CNN and other classification models. By changing the data dimension, the stability of each false comment recognition model was analyzed. The experimental results show that the TAB feature recognition method has better classification performance on logistic regression and support vector machine models, and the model is relatively stable, and the classification effect is not easily affected by dimension change.