When Support Vector Machines(SVMs) are applied in airborne LiDAR point data classification, their performance is limited by weak model sparseness, the prediction lack of probabilistic sense, and long training time. Therefore, a novel LiDAR point could data classification method was proposed based on an Informative Vector Machine (IVM). Firstly, the assumed density filtering was utilized to produce an approximation for probit classification noise model, and the classification problem was transformed into the regression problem. Then, the informative vectors of the active set in LiDAR point cloud data were chosen to achieve the model sparseness according to the largest posteriori differential entropy. Finally, in the training process, the kernel parameter was obtained by Marginal Likelihood Maximisation(MLM) and an One Against Rest (OAR) classifier was selected to realize multi-class classification. The LiDAR point cloud data from Niagara and Africa were selected for experiments in comparison with the SVM, and experimental results show that the classification accuracy of the method based on IVM increases to 94.20% and 90.78% respectively, the number of basis vectors reduce to 50 and 90 separately, and the training time decreases to 5.86 s and 8.03 s respectively. In conclusion, the classification method based on IVM has advantages in fast training speeds, strong model sparseness and high classification accuracy.