Abstract：With the rapid development of information technology, electronic information systems generate massive amounts of data across various fields. Traditional data processing methods face severe challenges in terms of real-time performance, accuracy, and depth of value mining. To address the information processing bottleneck in the big data environment, this study focuses on key strategies for big data analysis and processing in electronic information systems. By systematically reviewing existing technical frameworks, this study constructs an integrated processing model that integrates distributed storage, stream computing, and intelligent algorithms. This model emphasizes real-time collection and efficient cleaning of multi-source heterogeneous data, and introduces an improved parallel computing architecture to optimize the processing flow.
张旭 Xu Zhang (Wed,) studied this question.