Community classification method based on multi-dimensional graph convolutional neural network

The invention discloses a community classification method based on a multi-dimensional graph convolutional neural network, and the method comprises the steps: carrying out the preprocessing of pre-extracted community graph data; constructing a multi-dimensional graph convolutional neural network model based on the K-dimensional relation matrix, the L-layer graph volume network and the full connection layer; and finally, calculating a cross entropy loss value according to an output result of the full connection layer and a standard classification result, feeding the cross entropy loss value back to the multi-dimensional graph convolutional neural network, and repeatedly training until the model is converged. According to the method, a new K-dimensional adjacency matrix is defined, and a new multi-dimensional graph convolutional network model is constructed, so that deep connection among community members can be found, and the training speed and prediction accuracy of the model are improved under the condition