  The classification of news headlines is an important direction in the field
of NLP, and its data has the characteristics of compactness, uniqueness and
various forms. Aiming at the problem that the traditional neural network model
cannot adequately capture the underlying feature information of the data and
cannot jointly extract key global features and deep local features, a
dual-channel network model DC-EBAD based on the ERNIE pre-training model is
proposed. Use ERNIE to extract the lexical, semantic and contextual feature
information at the bottom of the text, generate dynamic word vector
representations fused with context, and then use the BiLSTM-AT network channel
to secondary extract the global features of the data and use the attention
mechanism to give key parts higher The weight of the DPCNN channel is used to
overcome the long-distance text dependence problem and obtain deep local
features. The local and global feature vectors are spliced, and finally passed
to the fully connected layer, and the final classification result is output
through Softmax. The experimental results show that the proposed model improves
the accuracy, precision and F1-score of news headline classification compared
with the traditional neural network model and the single-channel model under
the same conditions. It can be seen that it can perform well in the
multi-classification application of news headline text under large data volume.
