Named entity recognition method and device based on semi-supervised learning training

The invention relates to a named entity recognition method and device based on semi-supervised learning training, computer equipment and a storage medium. The method comprises the steps of obtaining annotation data and non-annotation data; performing supervised training on a sequence labeling model by utilizing the labeling data; calculating semantic vectors corresponding to the annotation data and the unannotated data through a trained sequence annotation model, and identifying the unannotated data in the same distribution as the annotation data according to the semantic vectors; calling a semi-supervised learning model, wherein the semi-supervised learning model is composed of the trained sequence labeling model and an auxiliary prediction network with a limited input view angle; and training the semi-supervised learning model through unlabeled data in the same distribution, and outputting a corresponding named entity recognition result through Viterbi decoding. By adopting the method, the data annotation c