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In order to improve the accuracy of point cloud target recognition, a target recognition model for point cloud based on Convolutional Neural Network (CNN) is proposed. Aiming at the problem that the existing deep convolutional point cloud target recognition network can not effectively extract the local topological features of point cloud, Iterative Farthest Point Sampling (FPS) combined with directional convolutional coding is used to capture the local shape features. In view of the instability of target recognition results caused by the rotation of point cloud data, the introduction of Spatial Transform Network (STN) enables point cloud data to self-adaptively perform spatial transformation and alignment. The experimental results show that the point cloud target recognition method proposed in this paper effectively improves the recognition accuracy which increases by 1.2% and 1.4% respectively compared with PointNet on ModelNet40 and ShapeNetCore datasets.