The invention relates to the field of text
classification methods, in particular to a text classification method fusing a mobile flip
bottleneck convolution network and a
capsule network of an adaptive feature channel, the text classification method is realized through an MBConv-CapsNet model, and the MBConv-CapsNet model comprises a mobile flip
bottleneck convolution module used for preprocessing a text matrix and obtaining a mobile flip
bottleneck convolution model; comprising a standard convolution layer, a channel-by-channel convolution layer, an SE
network layer and a point-by-point convolution layer. The improved
capsule network module is used for the text classification process and comprises an N-
gram convolution layer, a selective kernel
network layer, a main
capsule layer, a convolution capsule layer and a full-connection capsule layer, sequence information, word vectors and a context dependency relationship of the text can be fully considered, local and
global information of the text can be captured more effectively, and the text classification efficiency is improved. And the influence of
noise and redundant information is reduced, and an original text matrix is converted into more compact and more representative feature representation.