Pythagorean fuzzy sets (PFS) can better express and handle the uncertainty information and has the more lager representation space. Hence, the reasonable and effective method to measure the uncertainty of PFS can better analyze information. From the view of Dempster-Shafer evidence theory, hesitancy degree can include the two focal elements (member-ship, non-membership). Hence, considering the number of focal elements for hesitancy degree to measure uncertainty is important. In addition, the difference between membership and non-membership degree plays an essential role in uncertainty measure. From the above views, the paper proposed the new uncertainty measure. Based on the new uncertainty measure, cross entropy and divergence of PFS can be presented. In addition, some numerical examples are used to explain the proposed methods by comparing other methods. Finally, the proposed divergence can be used in pattern recognition to verify its effectiveness.
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