Given the characteristics of animal disease data, such as multi-source heterogeneity, spatiotemporal correlation, and small-sample imbalance, this study constructs and implements a multi-module collaborative intelligent analysis and early warning platform for animal diseases (ReEpi). Covering the entire process of "data-analysis-early warning-visualization", the platform integrates functional modules including data governance, statistical analysis, epidemiological modeling, molecular evolution analysis, spatial geographic analysis, intelligent diagnosis, and risk early warning. It adopts a reusable and loosely coupled architecture, combined with the Streamlit frontend and Python scientific computing ecosystem, ensuring both usability and scalability. Integrated tests and preliminary applications show that the platform performs well in accuracy, stability, and interactive experience, and can effectively support animal disease analysis and auxiliary decision-making in the context of agricultural big data.