To improve the application adaptability of the method used for solving the echo time of pulse laser ranging, this study transforms the echo time solving problem into a waveform classification problem and uses a novel deep learning method to solve the echo time. Further, a one-dimensional convolutional neural network model is trained by simulating the sample echo data containing different distances, signal amplitudes, waveform shapes, and noises with a time resolution of 0.1 ns, and a classification accuracy of 99.85% is obtained using the sample test set. Using the deep learning method and the Gaussian fitting method to process the airborne lidar echo data, the correlation coefficient of the wall surface sweep measurement results is 0.99981. Further, the plane fitting residuals of the field flight test data are approximately 20 mm; the effects of the two methods are observed to be equivalent. The results denote that the proposed method can satisfy the requirements for solving the echo time of airborne pulse laser ranging and can improve the solution accuracy and adapt to several application scenarios.