Rice crop rotation systems play a significant role in improving soil health and crop yield. This paper aims to explore the application of big data technology in optimizing rice crop rotation systems, to enhance their efficiency through data-driven decision models and optimization algorithms. Firstly, the paper reviews the current status of traditional rice crop rotation systems, analyzing their advantages and disadvantages, and their impact on soil health and rice yield. Then, it discusses the application of big data in agriculture, including data collection, processing, and analysis methods. The paper goes on to analyze in detail the strategies for rotating rice with other crops, explaining the effects of different rotation patterns on soil health and crop yield. Additionally, the paper proposes optimization strategies for crop rotation systems based on big data, introducing data-driven rotation decision models and the design of intelligent rotation systems. Through a virtual case study, the specific application of rotating rice with legumes is simulated, demonstrating the potential and effectiveness of big data technology in optimizing crop rotation systems. Finally, the paper discusses the challenges in data collection and processing, the accuracy and feasibility of the models, and provides an outlook on the future research directions for intelligent rotation systems.