How to obtain negation knowledge is a crucial topic, especially in the field of artificial intelligence. Limited work has been done on the negation of a probability distribution, which has been studied in depth throughout the literature. However, the aspect of the intensity level of negation enforcement has not yet been investigated. Moreover, let us note that the main characteristic of intelligent systems is just the flexibility for the sake of being able to represent knowledge according to each situation. In general, researchers have a tendency to express the need for cognitive range in the negation. Thus, it would seem very useful to find a wide range of negations under intensity levels in a probability distribution. Based on these ideas, this paper first proposes a new approach of finding a probability distribution negation and gives a domain of intensity in which the negation is executed, which is called the negation space. Then, we investigate a number of desirable properties and explore their correlation with entropy. Numerical examples show the characteristics of the proposed negation solution. Finally, we validate the efficiency of the proposed method from the point of view of the Dempster- Shafer belief structure.