湍流演化过程的准确预测对于理解湍流具有重要意义。现有基于机器学习方法的湍流研究多集中于湍流进入统计稳态后的统计行为预测，难以刻画湍流初始态到统计稳态整个演化过程。为解决这一问题，本研究提出采用动力学引导扩散模型（DYffusion）对Kolmogorov湍流从初始分布到统计稳态整个演化过程进行了预测，并将其与Markov Neural Operator（MNO）、使用教师强迫方式训练的U-Net进行了比较。结果表明，与MNO和U-Net相比，DYffusion不仅能够精确再现Kolmogorov湍流的演化过程，还能准确预测最终的统计稳态分布。

Accurate prediction of turbulence evolution is crucial for understanding turbulence. Existing machine learning-based turbulence research largely focuses on predicting the statistical behavior of turbulence after it reaches statistical steady state, failing to characterize the entire evolutionary process from the initial state to statistical steady state. To address this issue, this study proposes a dynamic guided diffusion model (DYffusion) to predict the entire evolution of Kolmogorov turbulence from its initial distribution to statistical steady state, and compares it with methods such as Markov Neural Operator (MNO) and U-Net trained using a teacher-forced approach. The results show that, compared to MNO and U-Net, DYffusion can not only accurately reproduce the evolutionary process of Kolmogorov turbulence but also accurately predict the final statistical steady-state distribution.