In order to improve the accuracy of ocean-land waveform classifications of airborne green lasers in complex ocean-land environments, an ocean-land waveform classification method based on multichannel weighted voting [i.e., multichannel weighted voting convolutional neural network (MWV-CNN)] is proposed. First, the multichannel green laser waveforms collected in the deep and shallow channels are input into the proposed one-dimensional convolutional neural network (1D CNN) module through a multichannel input module. Second, each 1D CNN module processes each channel waveform separately to obtain the predicted scores for each channel waveform belonging to the ocean and land categories. Finally, the predicted score of each channel is treated as weight, and a multichannel fusion module is used to determine the final waveform category via weighted voting. The measured data in the coastal waters of Lianyungang, China are verified by experiment using Optech CZMIL. The results indicate that the overall classification accuracy, Kappa coefficient, and overall accuracy standard deviation of MWV-CNN are 99.45%, 0.982, and 0.02%, respectively, and as compared with traditional ocean-land waveform classification methods, the proposed method exhibits better classification accuracy and robustness, thus providing a new effective way for realizing ocean-land waveform classification of airborne green laser with high accuracy.