Classification of sea and land returns in airborne lidar was essential for the research of coastal zones and their changing nature. A method for classification using deep learning on the original airborne lidar echo was proposed. A fully connected neural network, and a one-dimensional convolutional neural network (CNN), were used on a training dataset and test datasets from in-situ measurements, and a classification accuracy of 99.6% was obtained. The model was utilized on the datasets from different areas, a classification accuracy of 95.6% was achieved and the processing speed was increased by about 52% compared to support vector machine (SVM) method. The results denote that the deep learning method is very effective for classification of airborne lidar echo waveforms with high precision and speed. It may present further use as a candidate method for classifying species on the sea floor with airborne laser bathymetry.