Link prediction on knowledge graph is the main task of knowledge base completion, predicting whether a relationship existing in the knowledge base is likely to be true. However, traditional knowledge link prediction models are only appropriate for static data rather than temporal knowledge base. Temporal knowledge base exists on various fields. Take medical medicine field as example, diabetes is a typical chronic disease which evolves slowly. Thus, link prediction on clinical knowledge base such as diabetic complication requires the analysis on temporal characteristic of temporal knowledge base, which is a great challenge for traditional link prediction models. Thus, to address the prediction of temporal knowledge base, this paper proposes a long short-term memory (LSTM) based model for temporal knowledge base. The proposed model adopts memory cells of LSTM for sequential learning, and then builds incremental learning layer. Afterwards, timing characteristics can be extracted by the way of end-to-end, which realizes the prediction on temporal knowledge base. In experiments, the proposed model in clinical temporal knowledge base shows significant improvements compared with baselines including Rescal, NTN, TransE, TransH, TransR and DNN.