The invention discloses a text classification method based on a
capsule network. The problems that in the prior art, the overall precision is not high, the applicability is not high, a large amount ofimportant information is lost in the
feature extraction process, and the relation between the local part and the overall part in a text is ignored are solved. The method comprises the following steps: 1, nodes in a
capsule network being capsules consisting of a group of neurons, executing complex internal calculation on input by using matrix capsules, outputting instantiation parameters from results in a
matrix form, and meanwhile, outputting an activation value of each
capsule is output; 2, calculating between two adjacent
layers in the capsule network through an EM
routing algorithm; representing a higher-dimensional concept through
Gaussian cluster, and each activation capsule selecting a capsule of the next layer as a father node through an iterative routing process, so that link prediction is realized between two adjacent
layers of networks; and 3, training a weight parameter of the full connection layer, and calculating the
prediction probability of the real
label by using a softmax
activation function.