Analysis of Effective E-Commerce Coordination Big Data Processing Strategies under Infinite Deep Neural Network Topology

This paper researches and analyses the effective e-commerce coordination big data processing strategy through the infinite-depth neural network topology. This paper firstly proposes a neural network training model Neural Network-Storm (NN-S) based on Storm streaming distributed architecture, which decomposes the neural network training task into multiple computing units by data-parallel method, the parameters are updated synchronously after the training of a single batch of data is completed. In the Storm architecture, a Zookeeper network is used for multi-server distributed deployment. The training results show that the NN-S model can significantly improve the training speed of neural networks. At the same time, the NN-S architecture can quickly recover from node failures and network resource scheduling abnormalities with strong robustness. In this paper, we investigate the streaming-based distributed neural network training and design a Storm-based distributed neural network training model and optimized training algorithms, which are of reference significance for distributed neural network training.