准确的净空监测对于风电机组的安全稳定运行至关重要。然而，现有净空监测装置往往成本高昂且可靠性不足。针对这些挑战，提出一种基于深度学习的代理模型预测流程。首先，利用OpenFAST对自定义机组进行仿真，生成高质量的数据集，以克服传统测量方案中噪声大和精度低的问题。随后，通过特征工程降低模型训练难度，针对分类与回归树 （CART）模型，通过分风速段训练和大变形样本加权优化，实现了模型整体预测精度和峰值预测精度大幅提高的双重目标。结果显示：改进后的回归树模型，在整体预测精度（RMSE)上平均提高了7.51%，而且在峰值预测指标正误差超过0.5m和1.0m的比例分别降低了17.8%和35.4%。在涵盖切入到切出风速的100min测试结果中，Segmented-Weighted模型峰值最大预测正误差为0.402m、整体预测误差NRMSE为1.49%、正误差大于0.5m和1.0m样本比例为10.2%和1.69%，该流程为风机净空监测提供了一种高效、可靠且经济的解决方案。

Accurate clearance monitoring is critical to the safe and stable operation of wind turbines. However， existing clearance-monitoring systems are often costly and suffer from limited reliability. To address these challenges， this study proposes a deep-learning-based surrogate-model workflow for clearance prediction. High-fidelity simulations of a custom turbine are first performed using OpenFAST to generate a high-quality dataset that mitigates the high noise and low accuracy inherent in conventional measurement approaches. Feature engineering is then applied to reduce model training complexity. For the classification and regression-tree （CART） model， we introduced wind-speed-segmented training and a sample-weighting scheme that emphasizes large-deformation cases， thereby jointly improving overall predictive accuracy and peak-value fidelity. Results show that the modified CART model achieves， on average， a 7.51% reduction in RMSE； the proportion of positive prediction errors exceeding 0.5m and 1.0m are reduced by 17.8% and 35.4%， respectively. On a 100-minute test covering cut-in to cut-out wind speeds， the Segmented-Weighted model attains a maximum positive peak prediction error of 0.402m， an overall NRMSE of 1.49%， and proportions of positive errors greater than 0.5m and 1.0m of 10.2% and 1.69%， respectively. The proposed workflow provides an efficient， reliable， and cost-effective approach for wind-turbine clearance monitoring.