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Business intelligence makes good sale prediction crucial in any commercial activity as it has a significant impact on production and supply plan. However, practical commercial data presents explicit constraints, that how to get the optimal forecasts of commodity sales under the constraints is a vital problem many researchers face. The present research proposes a prediction model which combines graph convolution neural network and node bipartite graph. Firstly, the node bipartite graph algorithm is used to merge the constraint graph and the store graph, obtaining the “store-constraint bipartite graph”. Secondly, a graph convolutional neural network integrating GRU and AR is utilized to extract temporal features (X). Finally, a fully connected network is applied to predict the optimal solution (Y) after constraint optimization. The former can effectively learn complex features of stores, meanwhile, the later combines the constraint conditions with the store, which can effectively predict the sales of goods under the constraint conditions. In terms of model performance, we compared the proposed model with the classical method such as SVR, LSTM, ARIMA. RMSE, MSE, MAE and MAPE are used for evaluation indexes, and the results show that MAPE for one month’s sales of some product from both datasets is 7.75%.

This study aims to find a robust method to improve the accuracy of online sales prediction. Based on the groundings of existing literature, the authors proposed a Dependency SCOR-topic Sentiment (DSTS) model to analyze the online textual reviews and predict sales performance. The authors took the online sales data of tea as empirical evidence to test the proposed model by integrating the auto-regressive review information model into the DSTS model. The findings include: 1) the effect of the distribution of SCOR-topic from reviews on sales prediction; 2) the effect of review text sentiment on sales prediction increases as the specific topic probability dominates; and 3) the effect of review text sentiment on sales prediction increases as the rest topic probability evenly distributes. These findings demonstrate that the DSTS model is more precise than alternative methods in online sales prediction. This study not only contributes to the literature by pointing out how the distribution of sentiment topic impacts on sales prediction but also has practical implications for the e-commerce practitioners to manage the inventory better and advertise by this prediction method.