Hybrid IoT and Data Fusion Model for e-Commerce Big Data Analysis

Abstract

This article is aimed at studying the e-commerce big data recommendation model based on data fusion and the Internet of Things. This article chooses an embedded system for the construction of an e-commerce platform and uses data fusion technology to collect, transmit, and filter useful information from various information sources. Then, the collected information and data are analyzed and integrated, and visualization algorithms are used to better present data analysis, and association rules and structural similarity methods for electronic comparison are uses. This article uses the B/S architecture to design the overall framework of the data access layer, business logic layer, and user presentation layer; collects, organizes, stores, and presents the acquired consumer information; and finally analyzes the e-commerce background, platform performance, and supply and demand analysis. The experimental results show that the average clustering coefficient of the platform (0.7559) is smaller than the average clustering coefficient value of 8 items in the store (0.811) and smaller than the average network diameter and average path length of the online store (3.86, 7.7). Store products are better than store products, and the diameter and length of the product should be larger (2.71, 5.75). The recall rate of the e-commerce big data platform and model matching review method designed in this paper is 5% higher than that of the word matching model method and has a better expected effect in terms of user supply and demand.