“By reading only the title and abstract, do you think this research will be accepted in an AI conference?” A common impromptu reply would be “I don’t know but I have an intuition that this research might get accepted”. Intuition is often employed by humans to solve challenging problems without explicit efforts. Intuition is not trained but is learned from one’s own experience and observation. The aim of this research is to provide intuition to an algorithm, apart from what they are trained to know in a supervised manner. We present a novel intuition learning framework that learns to perform a task completely from unlabeled data. The proposed framework uses a continuous state reinforcement learning mechanism to learn a feature representation and a data-label mapping function using unlabeled data. The mapping functions and feature representation are succinct and can be used to supplement any supervised or semi-supervised algorithm. The experiments on the CIFAR-10 database show challenging cases where intuition learning improves the performance of a given classifier. © Springer Nature Switzerland AG 2020. All rights reserved.