Point cloud classification is an important stage in the application of airborne LiDAR point cloud in urban modeling and road extraction. Although there are many methods for point cloud classification, there are still some problems such as multi-dimensional feature vector information redundancy and low accuracy of point cloud classification in complex scenes. To solve these problems, a point cloud classification method is proposed based on multi- entity eigenvector fusion. The method extracts the feature vectors based on point entity and object entity and classifies the point cloud data by using random forest combined with color information. The experimental results show that the proposed multi-entity classification method is more accurate than the single-entity classification method. In order to further analyze the validity of random forest for point cloud classification, the support vector machine (SVM) and the back propagation (BP) neural network are used for a comparative analysis. The experimental results show that the three groups of point cloud classification results obtained by the random forest method are higher than those by the other two methods in the recall rate and F1 score.