E-Commerce Intelligent Logistics Data Based on Neural Network Model

Abstract

Not only e-commerce is developing rapidly, but also intelligent logistics technology is becoming more and more mature. In daily life, we can track the logistics information synchronously. On the smartphone app, the logistics information can be viewed at any time. However, the current processing algorithms are not enough for the exponentially increasing data volume and increasingly complex data types. This paper aims to analyze the massive data generated by e-commerce intelligent logistics. In this paper, a new classification algorithm is proposed, which is improved on the ordinary ladder classification algorithm, and artificial neural networks are added for automatic iterative update learning, which can automatically classify a large amount of data. The experimental results show that the classification error rate of the improved algorithm is less than 5%, and when the sample size is less than 30,000, the improved algorithm can significantly outperform the original algorithm.