The high dimensionality of hyperspectral LiDAR data in the spectral dimension, which includes a large number of bands or frequency bands, it is easy to overlook useful information in the video spectral band, resulting in poor signal sorting performance of hyperspectral LiDAR. Therefore, a study on hyperspectral LiDAR signal sorting based on improved random forest is proposed. Firstly, the variational modal decomposition algorithm is used to denoise the noisy signal of hyperspectral lidar; Then, a long and short term memory neural network algorithm is used to extract features from the denoised hyperspectral LiDAR signal, and a self coding neural network is used to reconstruct the extracted features to obtain the reconstructed radar signal features; Finally, the random forest algorithm is used to complete signal sorting based on the characteristics of hyperspectral LiDAR signals. The experimental results show that the SNR of the proposed method is 30.648 dB, and the RMSE is 0.149 8. The predicted sorting category is almost consistent with the actual sorting category, and the analysis time does not exceed 5 s, indicating that the proposed method has good sorting performance and practicality.