Automatic annotation of text sequence can address the common issue of high manual annotation labor cost in deep learning. In this paper

a representation model based on the entity boundary matrix (EBM) is constructed. On the basis

we propose an automatic annotation algorithm combining deep learning with KNN annotation correction algorithm (KNN-CA) where the manual labeling training set is not required. Firstly

the offline feature library and online feature library is built and initialized respectively with the utilization of collecting estate dataset. In addition

EBM is solve

d by matching algorithm and optimized via KNN-CA technique. After the data augmentation process

a training dataset of automatic annotation is obtained. Then the BiLSTM-CRF deep learning model is trained and all unlabeled annotation sequence is predicted. Eventually

the annotation sequence of solvable EBM is optimized via KNN-CA again so as to construct a sequence annotatied corpus dataset which is suitable for the identification of Chinese Geospatial Named Entities (CGSNE) and related researches. The experiment demonstrates that

F

1 score of labeled data reaches 95.35%.