Existing interactive segmentation methods mainly focus on opti mizing user interacting strategies, as well as making better use of clicks provided by users. However, the intention of the interactive segmentation model is to obtain high-quality masks with limited user interactions, which are supposed to be applied to unlabeled new images. But most existing methods overlooked the general ization ability of their models when witnessing new target scenes. To overcome this problem, we propose a life-long evolution frame work for interactive models in this paper, which provides a possible solution for dealing with dynamic target scenes with one single model. Given several target scenes and an initial model trained with labels on the limited closed dataset, our framework arranges sequentially evolution steps on each target set. Specifically, we propose an interactive-prototype module to generate and refine pseudo masks, and apply a feature alignment module in order to adapt the model to a new target scene and keep the performance on previous images at the same time. All evolution steps above do not require ground truth labels as supervision. We conduct thorough experiments on PASCAL VOC, Cityscapes, and COCO datasets, demonstrating the effectiveness of our framework in solving new target datasets and maintaining performance on previous scenes at the same time.