Low-altitude turbulence exhibits significant locality and spatiotemporal intermittency, making accurate prediction challenging for traditional forecasting models. Therefore, a hybrid expert model is proposed in this study to achieve low-altitude turbulence prediction. To capture the global patterns of turbulence samples, the baseline expert in the hybrid expert model leverages a temporal convolutional network (TCN) to learn the dynamic variation trends of the entire dataset. For the differentiated modeling of nonlinear characteristics of extreme turbulence samples, the residual expert screens extreme turbulence samples through parameter sensitivity analysis experiments, and takes the residual between the baseline predicted values and the true values as the learning target to correct prediction deviations. Meanwhile, to address the issue that the dual-expert fusion strategy with fixed weights struggles to adapt to different turbulence patterns, a learning gate with multi-feature fusion as input is designed. Dynamic weights are generated through a multilayer perceptron (MLP) structure to adaptively fuse the prediction results of the two experts, thereby achieving both the stability of prediction results for all samples and the prediction accuracy for high-value severe turbulence samples. Experiments are conducted using lidar observation data from Kashgar Airport in Xinjiang and Lanzhou Zhongchuan International Airport, and the overall accuracy of the proposed method reaches 96.5% and 92.7%, respectively. The proposed method provides a referable approach for the real-time prediction of low-altitude turbulence and aviation safety early warning.