[Objective] This paper proposes a model combining head and tail pointers with active learning, which addresses the sparse sample issues and helps us identify long terms on weapons. [Methods] Firstly, we used the BERT pre-trained language model to obtain the word vector representation. Then, we extracted the long terms by the head-tail pointer network. Third, we developed a new active learning sampling strategy to select high-quality unlabeled samples. Finally, we iteratively trained the model to reduce its dependence on the data scale. [Results] The F1 value for long term extraction was improved by 0.50%. With the help of active learning post-sampling, we used about 50% high-quality data to achieve the same F1 value with 100% high-quality training data. [Limitations] Due to the limitation of computing power, the data set in this paper was small, and the active learning sampling strategy requires more processing time. [Conclusions] Using head-tail pointer and active learning method can extract long terms effectively and reduce the cost of data annotation.