Continual learning (lifelong deep learning) is a machine learning paradigm within deep learning that focuses on enabling a model to learn sequentially from a stream of data or tasks arriving over time, while retaining knowledge acquired from previous data or tasks. The central objective is to prevent catastrophic forgetting, the phenomenon where learning new information causes the model to forget previously learned information. This approach aims to develop models capable of accumulating knowledge and improving performance across a lifetime of experiences, contrasting with the traditional static training paradigm on a fixed dataset. It is significant for developing adaptive systems that can operate effectively in dynamic environments without requiring complete retraining on the entire historical dataset.