【Objective】 Using space-borne LiDAR waveform data and the fuzzy pattern recognition algorithm, combined with the waveform characteristic parameters proposed in this paper, the forest type was identified and studied to improve the accuracy of forest type classification. 【Method】 First, using the differences in GLAS-received waveforms of the canopies of different forest types, the waveform characteristic parameters R_{\mathrm{fi}{\mathrm{t}}_1} , K_1 \text{'} and \overline{K_1} were acquired. Second, the extracted waveform feature parameters were combined with the other waveform feature parameters to establish a combination of waveform feature parameters. Thereafter, the waveform feature parameters of the sample data were subjected to index normalization and singularity detection processing to unify the dimensions of different waveform feature parameters and remove the sample data. Finally, combined with the fuzzy pattern recognition algorithm, the classification accuracy of different forest types was calculated. 【Result】 The total classification accuracies of coniferous and broadleaved forest types were 96.30%, of which the classification accuracies of coniferous and broadleaved forest types were 92.86% and 97.50%, respectively. Furthermore, the total accuracy was 84.51%, of which the classification accuracies of coniferous forest, broadleaved forest, and mixed forest types were 85.71%, 97.50% and 52.94%, respectively. 【Conclusion】 The fuzzy pattern recognition algorithm has certain advantages for the forest-type classification. This is especially so in the classification of coniferous and broadleaved forests, where the classification accuracy was higher.