Time information runs through the entire process of the creation， development and extinction of geological entities， reflecting the state and evolution of geological entities. In particular， the expression of geological time is usually related to metallogenetic mechanism and space time evolution regularity. This paper designs and implements a universal time and geological time information extraction method based on deep learning methods. Combining the description characteristics of time information in the Chinese text of geological and mineral resources， the time information in geological reports and documentation is divided into two types： universal time information and geological time information， and the two types of time information are subdivided. The self-developed geological time information corpus is constructed using cross-validation and opinion feedback mode. The time information extraction method based on BiLSTM-CRF is realized， and this method is compared with CNN and CRF. The experimental results show that the BiLSTM-CRF model is better than the mainstream model in time information extraction， and the F1-Measure of the overall time extraction reaches 95.49%， which solves the problem of standardized expression and structured extraction of time information in geological text.