Research on Pricing Strategy of Vegetable Commodities Based on BP Neural Networks

Abstract

This study aims to optimize the replenishment strategy of vegetable category in fresh supermarkets and explore the relationship between vegetable sales volume and pricing by analyzing the sales data using EXCEL, SPSS, and MATLAB. In data preprocessing, we dealt with missing values and abnormal data, and found that the flower and leafy vegetables had the largest and stable sales share. After the unsatisfactory linear fitting model through SPSS, we turned to BP neural network for pricing prediction, considering the correlation between time and sales volume. Our findings provide a reference for superstores to develop more accurate pricing strategies, which can help improve their competitiveness and sustainability to meet the challenges of market changes.