By utilizing Unmanned Aerial Vehicle (UAV) Hyper-Spectral Imaging (HSI) and Light Detection and Ranging, this study aims to investigate the classification methods of wetland vegetation in the Yellow River estuary using LiDAR data. However, due to the high spatial resolution HSI spectral variability and uneven LiDAR point cloud density, the classification results exhibit a "pepper and salt" phenomenon. To address these issues, this paper proposes a two-branch convolutional neural network (SSF-C-DBCNN) that integrates empty spectrum feature fusion and channel attention mechanism. The spectral attention mechanism mitigates the impact of spectral variability by assigning different weights to each band. Meanwhile, the spatial attention mechanism focuses on learning and emphasizing dense point cloud regions with strong feature expression ability in order to alleviate the influence of uneven LiDAR point cloud density on the results. Finally, the channel attention mechanism is introduced for extracting deeper features after two-branch feature fusion. Experimental verification using HSI and LiDAR data collected by UAV demonstrates that the proposed method outperforms random forest as well as five deep learning methods, yielding more suitable classification results for actual land cover while effectively suppressing the "pepper and salt" phenomenon.