Digital Logistics Business Operating System Based on Data Mining

In the process of socialized reform, building a large logistics system that adapts to the new situation is necessary to improve the self-development ability of university logistics. This article further promoted the reform of university logistics by studying the characteristics of university logistics data and their interactions through data mining algorithms related to logistics entities and independent colleges. The data mining methods for logistics services play a crucial role in promoting the scientific development of logistics management, as well as in promoting the reform of university logistics. The article used crowdsourcing algorithms to build a data mining system to process logistics business information in universities. According to the experimental results, the accuracy of the system in this article was 95.6 %, and the precision could reach 96.7 %. After changing the testing conditions, the precision, accuracy, and learning rate of the system in this article were also higher than other algorithms.