Building extraction technology in urban areas has been a hot topic in recent years, but how to accurately distinguish vegetation, buildings, and man-made objects and improve classification accuracy has always been a difficult point. Aiming at the problem of low classification accuracy, we propose a point cloud classification algorithm based on random forest. First, the improved cloth filtering algorithm is used to perform ground filtering on the point cloud data. And a decision tree is constructed and the correlation analysis based on the largest mutual information coefficient is performed to select the decision tree with the smallest correlation coefficient and the highest accuracy to obtain a weakly correlated random forest model. The decision results are processed by weighted voting, and finally a point cloud classification algorithm combining cloth filtering and weighted weakly correlated random forest is obtained. Compared with the traditional random forest classification algorithm, the algorithm is verified by the Vaihingen urban dataset, and the classification accuracy is improved by 4.2%.