The Decision of Product Market Forecasting and Delisting Based on PSO-BP

Objective: Aiming to construct a product sales forecasting model with higher forecasting accuracy to make guidance on product delisting and maximize product sales revenue. Methods: This paper uses the particle swarm optimization (PSO) algorithm to optimize the back propagation (BP) neural network of the sales model, and selects the iPhone 11 cell phone sale record as the data set for experimental simulation of the sales forecasting model. The delisting model makes forecasting on the sales revenue of the same cell phone with different memory in a future period. A reasonable product delisting time scale is set, and the delisting time point is determined in conjunction with the revenue situation. Results: The empirical results show that all evaluation indexes of the forecasting accuracy of the PSO-BP neural network forecasting method used in this paper can be improved by more than 20%. Meanwhile, when cell phones with a different memory of the same model are sold in the same market, the cell phones with the medium level of memory have a longer life cycle than those with large and small memory, and the total revenue obtained from selling the cell phones with the middle level of