Abstract: In order to solve the problems in the field of geographic information services （GIServices）， such as lack of corpus， nesting of multiple entities， and semantic sparser， etc.， in our report， a set of document entity labeling specifications for geographic information services was designed and the corpus in this field was constructed. Based on the traditional entity recognition model BiLSTM-CRF， the BERT （Bidirectional Encoder Representations from Transformers） pre-training model and convolutional layer were introduced， the BERT-1DCNN-BiLSTM-CRF model was proposed to improve the accuracy of the document entity recognition in geographic information services. In the word embedding layer of this model， the traditional static language model was replaced by the BERT pre-training model， which can solve the problems that the geographic information service field lacks a large amount of training corpus and cannot represent richer sentence semantic information. Additionally， the word volume features was added to the BERT model to improve the ability to express local features of sentences and reduce the interference of sentence semantic sparseness. The results showed that the GIServices document entity recognition method， which integrates BERT and CNN model， is better than the traditional deep learning method， whose accuracy is as high as 0.8268. It can realize the automatic entity recognition of GIServices documents effectively， which extends the potential application of deep learning model based on BERT in GIServices domain entity recognition.