Machining feature recognition is the key technology to realize the integration of CAD/CAPP/CAM. To tackle the robustness problem of the traditional recognition pattern of machining features based on symbolic reasoning, this study proposes an automatic recognition method of machining features based on deep learning of machining surface point cloud data. Utilizing the PointNet point cloud recognition framework, the study constructs a convolutional neural network (CNN) for automatic recognition of machining features of machining surface point cloud data. By the collection of the machining surface sets from CAD models and sampling of them to form point cloud data, a three-dimensional point cloud data library is constructed which is suitable for the learning of the network framework. A recognizer of machining features can be obtained by the CNN network training, able to automatically recognize 24 kinds of machining features, with the accuracy being higher than 99%. The method is simple, efficient, and insensitive to the point cloud data with noise and defects. Furthermore, it has good robustness and recognition effect for the damage of machining surfaces caused by feature intersection.