As one of the most important data sources in the field of geological survey in China, geological survey texts contain a wealth of geological knowledge and descriptions of geological bodies and other key information, and accurate and effective extraction of geological entities in this field can provide the basis for geological knowledge graph and knowledge inference. In this paper, based on the description of the geological named entity recognition task, it is analysed that geological entities contain a large number of terminologies along with domain characteristics such as entity nesting and a large number of long entities, which further increase the difficulty of geological named entity recognition. A lightweight pre- training model (ALBERT) — bi- directional long and short- term memory network (BiLSTM) — conditional random field (CRF) model is proposed for geological named entity recognition. Firstly, ALBERT is used to model the contextual features of the input characters, and BiLSTM is used to further characterize the contextual features, and finally CRF is used to achieve annotated sequence prediction. The experimental results show that the proposed method has superior extraction performance than the mainstream named entity recognition model algorithms on the constructed geological named entity recognition datasets, and the proposed named entity recognition model can provide reference for domain entity recognition, as well as provide powerful methodological support for entity relationship extraction and geological knowledge graph construction in the geoscience domain.