To ensure the long-term reliable operation and performance optimization of wind turbine blades, real-time deformation monitoring is essential. Therefore, this study develops a LiDAR-based non-contact real-time deformation monitoring method for wind turbine blades using a cascaded structural fusion framework, enabling high-precision tracking of dynamic blade deformation. First a three-dimensional LiDAR is used to collect the point cloud of blade motion, and noise is eliminated through downsampling and statistical denoising preprocessing. Subsequently, a point cloud cascade processing framework of “prediction-correlation-optimization-registration” is established, which utilizes the KF dynamic model to predict the prior positions of feature points, introduces dynamic constraints and observation fusion mechanism, and effectively suppresses the error propagation of point cloud noise; Based on the prediction covariance constraint, KD tree adaptively matches the search domain to improve the accuracy of feature correspondence and enhance the robustness of feature matching under vibration conditions; Integrating point cloud observations to optimize feature coordinates, and inputting the optimized feature set into the ICP algorithm to solve high-precision deformation matrices, forming a closed-loop system that dynamically tracks to global registration, thus achieving a balance between accuracy and efficiency in a cascaded architecture. Experimental results show that the proposed method achieves a deformation measurement accuracy of 0.209 6 mm; 95% of data points have an absolute error of ≤ 0.1 mm, with RMSE = 0.073 2 mm, MAE = 0.057 8 mm,and a correlation coefficient of 0.954 4 (p < 0.01); In terms of system matching performance, the matching success rate reaches 95.4%, significantly better than the single KF algorithm (82.6%) and ICP algorithm (73.1%), verifying the effectiveness of this method in real-time deformation monitoring engineering. Under complex working conditions, it still has excellent measurement accuracy, system stability, and environmental adaptability, which can provide reliable technical support for wind turbine blade health management, early fault diagnosis, and intelligent operation and maintenance decision-making.