Abstract: Traditional airflow field measurement methods for cotton picker fans (e.g., Pitot tube method) can only obtain discrete point data and fail to collect full-flow field information; although Computational Fluid Dynamics (CFD) simulation enables full-flow field analysis, it is restricted by high computational cost and long cycle, making real-time monitoring difficult. To address these issues, this study proposed a fast and accurate dynamic monitoring method for the airflow field at the outlet of cotton picker fans based on Digital Twin (DT) technology. Firstly, a high-fidelity CFD model of the centrifugal fan was established, and unsteady simulations were carried out across 1000~4000 r/min to obtain full-flow field data including velocity and pressure distributions. Secondly, four surrogate models—Polynomial Response Surface (PRS), Radial Basis Function (RBF), Kriging (KRG) and Support Vector Regression (SVR)—were compared to select the optimal one for fast airflow field prediction, and the Fuzzy C-Means (FCM) clustering algorithm was introduced to screen representative core sampling nodes from the original dataset to reduce computational overhead. Thirdly, the optimized KRG model was encapsulated into a .NET standard class library by Matlab, and a Windows Forms (WinForm) Human-Machine Interface (HMI) platform was developed via C# hybrid programming. Finally, a pneumatic conveying test bench was built, equipped with a PNP Hall effect sensor for rotational speed measurement, and combined with a planar guide rail system to collect high-precision wind speed data at key nodes, completing system performance verification. CFD simulation results showed that fan rotational speed correlated with the average wind speed in the central flow domain: the average wind speed increased approximately linearly at low rotational speeds, while growth slowed with nonlinear characteristics at high rotational speeds. With rising rotational speed, the wind speed difference at the outlet increased and inhomogeneity intensified, reaching 68.86% at 3500 r/min. Among the four surrogate models, the KRG model performed best, with its Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) all lower than the others. Compared with uniform sampling, FCM clustering reduced training samples by 8.84%, improved response speed by 8.86% and decreased prediction error by 3.9353%. The monitoring system built with 821 clustered representative nodes had an average response time <1 s under all tested rotational speeds. Experimental verification indicated that the average prediction error between system output and measured values was within 9.26%, with the lowest error of 4.90% at 4000 r/min. Dense grid tests at 3500 r/min showed that the relative error of most measurement points was still below 8% even near the volute tongue, where airflow separation and eddies frequently occurred. The proposed monitoring method effectively integrates CFD simulation, surrogate modeling and FCM clustering technology, enabling fast and accurate full-flow field monitoring of cotton picker fans. Its real-time response and prediction accuracy meet agricultural machinery operational requirements, providing technical support for monitoring and optimizing cotton picker pneumatic conveying systems, offering a transferable reference for airflow field monitoring of other agricultural equipment, and promoting digital twin technology in agricultural engineering.