In view of current land cover classification methods using the full-waveform characteristic parameters from spaceborne lasers, land cover categories are misclassified because the extracted features cannot accurately distinguish various types of features. We propose a land cover classification method that considers the contribution of waveform feature parameters and the pooling scale. First, the waveform after preprocessing and Gaussian decomposition is characteristic extracted and roughly classified, and the importance of each waveform feature parameter is evaluated and sorted. The best feature parameter set is determined by backward selection of the waveform feature parameters. Moreover, the original waveforms are pooled at different scales, and the correlations between ground objects after pooling are compared with the correlations between the original waveforms to ensure the stability of the correlation between various waveforms of ground objects and the peak information of waveforms while reducing the amount of data. Finally, random forest (RF) and support vector machine classifiers are selected to complete the land cover classification experiment based on the optimal waveform parameter set and the optimal pooling scale. We use ICESat (ice, cloud, and land elevation satellite) and Geoscience Laser Altimeter System data from the Beijing–Tianjin–Hebeii region for verification. The results show that, based on the RF classifier, the overall classification accuracy was 86.95%, and the use of spaceborne laser waveform characteristic parameters compared with the classification method greatly improved the accuracy, fully verifying the feasibility of the method in classifying land cover types. These findings can provide a technical basis for the application and promotion of GaoFen 7(GF-7) and other follow-up, satellite-based laser altimetry data in the fine-grained classification of land cover types.