Electroencephalogram (EEG) is a very weak random signal with complex mechanism, which comprehensively reflects the activities and the functional states of brain tissue. Due to the weak characteristic of EEG, the traditional basic template method is a good tool for EEG analysis. In order to further enhance the performance of this method, we propose a new transfer entropy method based on adaptive template. The method improves the symbolization of time series based on the original basic template method. Numerical experiments show that the improved adaptive template method can obtain better dynamic characteristics, and also has better ability to distinguish the results in the analysis of time series. We use the transfer-entropy-based adaptive template method to analyze adolescent and adult EEG. We also study the relationship of the transfer-entropy-based adaptive template method to the total data length L and the data length l of the divided cells. Numerical results show that the transfer entropy value of adult EEG based on adaptive template is significantly higher than that of teenager EEG. This indicates that adult has a more significantly mental activity and the functional status of the brain is more complex. We then apply this method to human EEG signals and investigate their statistical properties. The results show that compared with the result of the basic method, the transfer-entropy-based adaptive template method can significantly show the EEG coupling for adolescents and adults EEG, which has a better discrimination and can better capture dynamic information and the change of the system dynamic complexity. At the same time, it will be more conducive to clinical diagnosis and provides a new and better method to judge whether brain is in a pathological state.