Design and Optimization of Business Decision Support System Based on Deep Learning

Abstract

Decision support system (DSS) is based on the data of daily business processing system, using mathematical or intelligent methods to analyze the data and comprehensively predict the future business trends. The deep learning model based on neural network architecture can effectively process and analyze big data. This paper constructs a business DSS based on deep learning and optimizes it. In the middle platform, AI and deep learning technology are used to dynamically model and predict commercial retail. A convolutional neural network (CNN) model based on network performance evaluation is proposed, which effectively improves the dynamic performance of CNN and improves its prediction ability. The system test results show that the integration of CNN prediction function into traditional DSS not only maintains the characteristics of traditional DSS, but also has the characteristics of neural network, makes full use of normative knowledge and empirical knowledge, and improves the intelligent level of traditional DSS. Help enterprises achieve their business goals more steadily, improve work efficiency, and reduce labor costs.