A Dynamic Prediction Neural Network Model of Cross-Border e-Commerce Sales for Virtual Community Knowledge Sharing

Abstract

In this paper, a neural network algorithm is used to conduct in-depth research and analysis on the sales dynamics prediction of virtual community knowledge sharing in cross-border e-commerce. Both the expected returns and the social network structure are analyzed, and both have positive effects on knowledge sharing in the actual development process, but the degree of them also possesses certain variability. A model of the factors influencing the quality of knowledge shared by users is constructed to explore the relationship between the dimensions in social capital and how they affect the community users’ perceptions of knowledge quality. Exploring the strong influencing factors of product repurchase rate has key implications for promoting product sales and sales forecasting. The scale of this paper has undergone several minor revisions, and the content validity is very good. Criterion validity is generally reflected by Person correlation coefficient, and construct validity includes exploratory factor analysis and confirmatory factor analysis. First, the values were clustered, and the optimal variables were selected using stepwise regression and fitted with Poisson regression models to explore the relationship between the repurchase rate of different products and the factors that strongly influence the repurchase rate in the case. To predict the sales of goods, the advantages of the BP neural network, LSTM neural network, and Verhulst gray model are combined: BP neural network can predict sales by combining the current data corresponding to independent variables, LSTM neural network can explore the influence of historical data on sales, and Verhulst model can predict sales based on the growth trend of variables. A BP-LSTM-Verhulst nested neural network based on the AP algorithm is constructed to predict sales volume, and the accuracy of this method is proved by example. Finally, it is found that the proposed sales prediction method has higher accuracy than exponential regression and shallow neural networks. The deep learning prediction method combining unstructured data such as images proposed in the paper not only provides a more accurate sales prediction method for short life cycle products in e-commerce but also provides an effective deep learning method for management practices. The KMO value obtained after the test is less than 0.5, which is not suitable for factor analysis. Using SPSS22.0 to expand the KMO value and Bartlett’s sphericity test, the KMO value is significantly greater than the minimum requirement of 0.5, the KMO value of self-efficacy is 0.826, and the KMO value of expected return is 0.870.

Optimizing E-commerce Inventory to prevent Stock Outs using the Random Forest Algorithm Approach

“…For example, a Q-learning algorithm model based on NN has been proposed for dynamic pricing problems in e-commerce product lines, demonstrating the applicability of NN in addressing e-commerce challenges [41]. Moreover, a neural network algorithm has been utilized for in-depth research and analysis on the sales dynamics prediction of virtual community knowledge sharing in cross-border e-commerce, highlighting the role of NN in predicting e-commerce sales dynamics [42]. Gradient Boosting (GB) has also been a prominent method in machine learning.…”

Analysis of Heavy Metal Pollution in Soil along the Shuimo River by the Grey Relational Method and Factor Analysis

“…When the sum of the squares of the simple correlation coefficients among all variables is much greater than the sum of the squares of the partial correlation coefficients, the value of KMO (Kaiser-Meyer-Olkin) is close to 1, and the closer the value is to 1, the stronger the correlation between the variables, and the more suitable the original variable is as a factor analysis; when the sum of squares of the simple correlation coefficients among all variables is close to 0, the KMO value is close to 0. The closer the KMO value is to 0, the weaker the correlation between variables, and the original variables are less suitable for the factor analysis [30][31][32]. Kaiser gave the commonly used KMO metrics, which are as follows: above 0.9 means very suitable; 0.8 means suitable; 0.7 means general; 0.6 means not suitable; below 0.5 means extremely unsuitable [33].…”

Redefining E-Commerce Experience

“…Overall, AR and VR can shape social responsibility concerns and purchasing intentions within the apparel industry. (Tian & Wang, 2022) develop a neural network algorithm for sales dynamics prediction of virtual community knowledge sharing in cross-border e-commerce. The proposed sales prediction method had higher accuracy than exponential regression and shallow neural networks.…”