AIoT applications often encounter challenges such as terminal resource constraints, data drift, and data heterogeneity in real world, leading to problems such as catastrophic forgetting, low generalization ability, and low accuracy during model training. To address these challenges, we proposed CoLLaRS, a cloud–edge–terminal collaborative lifelong learning framework for AIoT applications. In the CoLLaRS framework, we alleviate the problem of terminal resource constraints by uploading terminal tasks at the edge. CoLLaRS uses continuous training at the edge to achieve lifelong learning training of the model and solve the problem of catastrophic forgetting. CoLLaRS employs federated optimization in the cloud to perform personalized aggregation of different edge models and solve the problem of weak model generalization ability. Finally, the model is fine-tuned at the terminal to further optimize its accuracy in local tasks. Our experiments on real-world datasets showed that CoLLaRS has an 8% improvement in accuracy and a 5% improvement in backward transfer(BWT) and forward transfer(FWT) compared to other baseline algorithms. The results of the ablation experiments further confirmed the effectiveness of CoLLaRS. •A cloud–edge–terminal collaborative lifelong learning framework.•Higher accuracy and robust to catastrophic forgetting with similar training time.•Evaluated in real-world AIoT semantic segmentation datasets.