Modern defence requires intelligent perception algorithms to possess incremental and continuous learning capabilities in complex open scenarios， while traditional deep learning methods are based on closed training with the entire dataset， which limits their application ability and usage scope. Existing continuous learning algorithms face the problem of catastrophic knowledge forgetting. This paper proposes for the first time an incremental continuous target detection method based on the distillation of high-level semantic features， which guides the selection of high-value underlying features through high-level semantic features and distills the feature from the teacher model to the student model， thus effectively facilitating the transfer of knowledge of the old task and alleviating catastrophic knowledge forgetting. Experiments on the public image dataset MS COCO show that this method outperforms the previous best method for target detection in all types of continuous learning scenarios， which is expected to promote the generation of continuous learning capability and autonomous attempts of intelligent systems in open-world setting.