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The model\u2010agnostic metalearning (MAML) with the CACTUs method (clustering to automatically construct tasks for unsupervised metalearning) is improved as EW\u2010CACTUs\u2010MAML after integrated with the entropy weight (EW) method. Few\u2010shot mechanisms are introduced in the deep network for efficient learning of a large number of tasks. The process of implementation is theoretically interpreted as \u201cgene intelligence.\u201d Validation of EW\u2010CACTUs\u2010MAML on a typical dataset (Omniglot) indicates an accuracy of 97.42%, performing better than CACTUs\u2010MAML (validation accuracy\u2009=\u200997.22%). 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