With the objective of addressing the problem of the fixed convolutional kernel of a standard convolution neural network and the isotropy of features making 3D point cloud data ineffective in feature learning, this paper proposes a point cloud processing method based on graph convolution multilayer perceptron, named GC-MLP. Unlike traditional local aggregation operations, the algorithm generates an adaptive kernel through the dynamic learning features of points, so that it can dynamically adapt to the structure of the object, i.e., the algorithm first adaptively assigns different weights to adjacent points according to the different relationships between the different points captured. Furthermore, local information interaction is then performed with the convolutional layers through a weight-sharing multilayer perceptron. Experimental results show that, under different task benchmark datasets (including ModelNet40 dataset, ShapeNet Part dataset, S3DIS dataset), our proposed algorithm achieves state-of-the-art for both point cloud classification and segmentation tasks.