Laser scanners have been successfully employed for forest variables estimation. Nevertheless, the regular monochromatic LiDAR systems cannot capture enough information for tree species classification. Therefore, optical multispectral (MS) imagery or their integration with airborne LiDAR has been studied for classifying forest tree species. Modern multispectral airborne LiDAR has improved tree species identification accuracy compared to single-channel systems, by incorporating both structural and spectral information. Hence, the characterization of tree species is one of the primary and most popular potential applications of multi-wavelength laser scanning attracted attention in recent years. State-of-the-art deep learning (DL) algorithms have recently demonstrated promising potential in boosting the automation and accuracy of geospatial data processing. However, the effectiveness of DL models has never been explored in tree species classification using MS-LiDAR data, and the conducted studies have relied only on conventional handcrafted features. Therefore, the aim of this paper is to report our investigations on the capability of automatic DL-based features for unsupervised classification of tree species utilizing MS airborne LiDAR data. First, the point clouds of three missions with Green, NIR, and SWIR wavelengths were pre-processed and merged to create MS LiDAR datasets. Subsequently, individual trees were delineated using SLIC superpixel segmentation. A Convolutional Auto-Encoder (CAE) was used for extracting deep features from the MS LiDAR data. Then, several statistical features were extracted for each tree segment from deep ones. Finally, the Mini Batch K-means was used to cluster the resulting features and identify the tree species. The achieved results indicate the superiority of DL-based features over the manual ones for tree species classification, improving the overall accuracy and kappa coefficient by 5.63% and 8.32%, respectively. To the best of our knowledge, this is the first study exploring unsupervised DL for forest mapping using MS LiDAR and confirms its potential as a single-data source solution.