Chinese tourism field named entity identification method based on graph convolution neural network

According to the Chinese tourism field named entity identification method based on the graph convolution neural network, the graph convolution neural network comprises an input layer, an embedded layer, a graph convolution layer and a hierarchical structure, and an input body comprises a named entity and a non-entity; the method comprises the following steps: S1, simultaneously expanding to two sides by taking any non-entity of a tourism domain text as a center until a single character in a complete sentence is traversed; S2, extracting character features; S3, extracting character features; S4, inputting and training; S5, optimizing a graph convolution layer; S6, labeling all named entities in the tourism field text data, introducing a Laplace regularization loss function into the graph convolution layer so as to mine node internal structure information and extract character features; and S7, obtaining a hierarchical relationship between the named entity and the non-entity. According to the method, the charac