As point cloud acquisition technology develops and the demand for 3D applications increases, real-world scenarios require continuous and dynamic updating of the point cloud analysis network with streaming data. This study proposes a dual feature enhancement for the class-incremental 3D point cloud object learning method, which adapts point cloud object classification to scenarios where new category objects keep emerging in newly acquired data through incremental learning. This study proposes a discriminative local enhancement module and knowledge injection network respectively to alleviate new class bias problems in class-incremental learning by studying the characteristics of point cloud data and old class information. Specifically, the discriminative local enhancement module characterizes the various local structural characteristics of 3D point cloud objects by perceiving expressive local features. Subsequently, the importance weights of each local structure are obtained based on the global information of each local structure, enhancing the perception of differential local features and improving the differentiation of new and old class features. Furthermore, the knowledge injection network injects old knowledge from the old model into the feature learning process of the new model. The enhanced hybrid features can effectively mitigate the increased new class bias caused by the lack of old class information. Under the incremental learning experimental settings of the 3D point cloud datasets ModelNet40, ScanObjectNN, ScanNet, and ShapeNet, extensive experiments show that compared with existing state-of-art methods, the method in this study has an average incremental accuracy improvement of 2.03%, 2.18%, 1.65%, and 1.28% on the four datasets.