Application of BP Neural Network in Matching Algorithm of Network E-Commerce Platform

Abstract

In order to solve the matching algorithm problem of network e-commerce platform, a method of applying BP neural network in the network e-commerce platform matching algorithm is proposed. First of all, combined with the actual situation of the platform, select 9 factors that are most in line with the company’s actual business model to influence the selection for analysis; secondly, import 60 sets of data into MATLAB software, measure the input and output data uniformly, and divide the sample data matrix into training set and test. Finally, after multiple factor combinations and verifications, it is concluded that in the training model of the five main factors, the prediction results of the model are compared with the real values. The feasibility of establishing the selection model based on BP neural network is proved. Online e-commerce platforms can refer to this model to build a product selection model that meets the needs of the platform, helping enterprises to achieve more efficient product selection work. Since the parameter initialization of the neural network is random, although the output results are different after the program runs for many times, the R2 is still stable between 0.7 and 1.0, which proves that the predicted value made by the system is highly approximate to the real value and can achieve the predicted effect.