Application of Big Data Analysis Model in Intelligent Logistics

The logistics big data in this paper mainly studies the optimization and prediction accuracy of logistics order quantity, helps logistics enterprises to arrange vehicle scheduling in advance, reduces the transportation cost of vehicles, and makes a series of generalizations about customer satisfaction evaluation methods in logistics service industry, and how to use convolutional neural network model to classify its text. It is very necessary to apply efficient and stable prediction algorithm to logistics order quantity prediction, which is also the core aspect of today's logistics enterprises. How to effectively use logistics big data to create more value for logistics enterprises is a concern of logistics enterprises. In this paper, the research and analysis of design and neural network in logistics, first of all, the research is an evaluation method of customer satisfaction in customer satisfaction, is an evaluation index of the logistics service industry, and then through the research and analysis of a large number of continuous BP neural network algorithm literature at home and abroad, combined with niche particle swarm, continue to optimize the parameters, and finally get the final results.