A brain-inspired cognitive learning method can obtain good learning results in various environments and tasks by selecting the most suitable algorithm models and parameters based on the environments and tasks, and can correct wrong behavior. The framework includes four main modules: a cognitive feature extraction module, a cognitive control module, a learning network module, and a memory module. The memory module includes a data base, a cognitive case base, and an algorithm and hyper-parameter base, which store data of dynamic environments and tasks, cognitive cases, and concrete algorithms and hyper-parameter values, respectively. For dynamic environments and tasks, the most suitable algorithm model and hyper-parameter combination can be flexibly selected. In addition, with “good money drives out bad”, mislabeled data is corrected using correctly labeled data, to achieve robustness of training data.