Research on the Application of Hybrid Intelligent Systems in Data Fusion and Decision Support in Digital Commerce

In the context of rapid development of digital business and explosive growth of data volume, traditional data processing and decision support systems encounter a huge challenge in processing complexity and timeliness, and novel hybrid intelligent systems are indispensable to promote the efficiency and accuracy of data fusion and decision support. In this context, we present a hybrid intelligent system for data fusion and decision support in digital business. Initially, data from different sources were denoised and normalized using principal component analysis (PCA). Random forests then run on the data for preprocessing and feature extraction, automatically identifying the important features, and providing input for decision-making. Then sense the uncertainty data with fuzzy logic and generate multiple decision paths with fuzzy reasoning system. For the optimal algorithm is the genetic algorithm to optimize the decisionmaking path, the relevant knowledge of the expert system is combined to verify and adjust to ensure the rationality and practicability of the decision-making. It has been experimentally demonstrated to outperform standard models, providing substantial improvements in the speed of data processing and quality of decision-making.