The Analysis of E-commerce Data: Based on LoT Model

Abstract

Cross-border e-commerce provides a new channel for the development of foreign trade. However, in cross-border e-commerce trade, factors including language, culture, distance and others make the information asymmetry between buyers and sellers more prominent. And the transaction risk is more serious than other types of transactions. This paper analyzes the formation mechanism of sellers' default behavior and transaction data risk in cross-border e-commerce. Based on the theoretical model, the real transaction data of Amazon platform was used as the machine learning training set to derive the behavior function of buyers and sellers. Based on the complex adaptive system theory of simulation economics and agent swarm model. Also, this paper constructs an intelligent dynamic simulation system of cross-border e-commerce transaction risk under incomplete information situations and simulates the seller's default behavior. Finally the paper systematically analyzes the formation mechanism of multi-round cross-border e-commerce transaction risk under incomplete information situations by comparing the simulation results.