With the surge in the amount of data in the internal and external environment, the collection, analysis, processing, and storage of the increasing data sources and data volume, as well as the problems of big data management, are the current situation and dilemmas of data management faced by enterprises today. Cognitive science and big data technology can provide good auxiliary support for enterprise management decision-making. This study takes the business information management of cold chain logistics enterprises as an example, aiming at the characteristics of business intelligence data in real applications, based on cognitive science and big data technology, from low-cost and high-performance storage, security management, and big data analysis. This paper is mainly through the research of big data processing theory and key technologies. Based on analyzing the logistics industry’s data access rules and characteristics, this study proposes a hot data prediction model for multilayer hybrid storage systems. It is verified that the prediction model has good accuracy, robustness, and universality. For the application scenario of multitenant distributed data access, a data transparent security management model is proposed. Simulation experiments show that this method can realize data security management when the performance loss is controlled within an acceptable range. Based on the real-time computing technology of massive data, the label optimization scheme of collaborative filtering and reinforcement learning is used to realize the logistics distribution recommendation model and to solve the accuracy and real-time problems of logistics service distribution analysis.