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As we all know, price is a very important factor that affects product sales. To a certain extent, price cuts will increase sales, and price increases within the acceptable range of users can increase manufacturers’ profits. Therefore, for manufacturing companies, scientific product pricing has always been a tricky issue. The pricing strategy model of traditional manufacturing companies is generally based on traditional estimates and conduct price reduction promotions. At present, there are not many pricing strategy models with better scalability for manufacturing companies, especially there are not many quantitative models that can effectively evaluate the impact of competing products on this product. For manufacturing companies, the easiest way to affect sales is to modify product prices. Therefore, based on the relatively novel ConvLSTM neural network model, this article constructs a pricing strategy model for manufacturing companies. To build a pricing strategy model based on cross-domain and cross-brand data, the traditional LSTM model cannot capture the complex relationships between different dimensions of data. Therefore, this article introduces the improved ConvLSTM neural network model of the LSTM model into the field of pricing strategy, and first passes the relevant data through the convolutional layer before ConvLSTM to fully explore the hidden high-dimensional logical associations between the cross-manufacturer and cross-domain data. Therefore, this chapter uses the ConvLSTM model to predict sales based on cross-domain and cross-brand data. At the same time, statistical methods are used to check the confidence interval of the prediction results to enhance the reliability of the model. Finally, use the predictive model to traverse the reasonable pricing interval to obtain the simulated highest sales and optimal product pricing. This chapter finally verifies the superiority of the ConvLSTM-based pricing strategy model proposed in this chapter through design comparison experiments.
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