The invention provides a specific target
emotion classification method based on attention coding and a graph
convolution network, and the method comprises the steps: obtaining a context and a hidden
state vector corresponding to a specific target through a preset bidirectional
recurrent neural network model, and carrying out the multi-head self-attention coding of the context and the hidden statevector; extracting a
syntax vector in a
syntax dependency tree corresponding to the context by combining a point-by-point
convolution graph
convolutional neural network, and performing multi-head self-attention coding on the
syntax vector; then, multi-head interaction attention is used for carrying out interaction fusion on syntactic information codes, context
semantic information codes, syntacticinformation codes and specific target
semantic information codes; and splicing the fused result with the context
semantic information code to obtain a final feature representation, and obtaining an
emotion classification result of the specific target based on the feature representation. Compared with the prior art, the relation between the context and the syntax information and the relation between the specific target and the syntax information are fully considered, and the accuracy of sentiment classification is improved.