The invention provides an electronic official document entity extraction method. The electronic official document entity extraction method comprises the following steps: A, preprocessing; B, constructing features; C, training an entity extraction model; D, obtaining a corpus; E, obtaining a word vector; F, training an algorithm model. According to the method, a traditional sequence labeling algorithm and a deep learning algorithm are combined, the advantage that a traditional sequence algorithm needs less corpus labeling is utilized, a semi-supervised method is adopted to expand corpuses, andthe problem that time and labor are wasted when a large number of corpuses need to be manually labeled in the deep learning algorithm is solved. Maximum forward and reverse dictionaries, syntax and semantic features are added into the CRF model, and front and rear boundary word features of entity words are fully considered, so that the algorithm has generalization ability. A dilated CNN and BiLSTM-CRF are combined, the dilated CNN takes a character-level vector and a character-level position feature as external features, and the external features and a part-of-speech vector are spliced into aword vector, so that more semantics and up-and-down related information can be expressed to a certain extent.