Neutrosophic soft sets are a mathematical model put forward to overcome uncertainty with the contribution of a parameterization tool and neutrosophic logic by considering of information a falsity membership function, an indeterminacy membership function and a truth membership function. This set theory which is a very successful mathematical model, especially as it handles information in three different aspects, was first introduced to the literature by Maji (Ann Fuzzy Math Inf 5(1):157–168, 2013) and later modified by Deli and Broumi (J Intell Fuzzy Syst 28(5):2233–2241, 2015). In this way, they aimed to use neutrosophic soft sets more effectively for uncertainty problems encountered in most real life problems. Relations are a method preferred by researchers to explain the correspondences between objects. In this paper, neutrosophic soft relationships are discuss and define by referring to the theory of neutrosophic soft set proposed by Deli and Broumi (Ann Fuzzy Math Inf 9:169–182, 2015). Then, we present the concepts of composition, inverse of neutrosophic soft relations and functions along with some related properties and theorems. Moreover, the equivalence classes and equivalence relations of soft relations are given with support from real life examples and some of their properties are analyzed. Finally, we propose an algorithm to be used in expressing the correspondence between objects in solving uncertainty problems by using the soft relationship defined and an example is given to show how this algorithm can be applied for uncertainty problems.

中智软集是通过考虑信息的虚假隶属度函数，不确定性隶属度函数和真实性隶属度函数而提出的，用于克服不确定性的数学模型，该模型通过参数化工具和中智逻辑来解决。这种集合论是一个非常成功的数学模型，尤其是它处理三个不同方面的信息时，最早是由Maji（Ann Fuzzy Math Inf 5（1）：157–168，2013）引入文献的，后来由Deli进行了修改。和布鲁米（J Intell Fuzzy Syst 28（5）：2233–2241，2015）。通过这种方式，他们旨在更有效地使用中智软集合来解决大多数现实生活中遇到的不确定性问题。关系是研究人员用来解释对象之间的对应关系的一种方法。在本文中，通过参考Deli和Broumi提出的中智软集合理论，讨论并定义了中智软关系（Ann Fuzzy Math Inf 9：169–182，2015）。然后，我们介绍了组成概念，中智软关系的逆和功能以及一些相关的性质和定理。此外，在现实生活中的实例的支持下，给出了软关系的等价类和等价关系，并分析了它们的一些性质。最后，我们提出了一种算法，该算法通过使用定义的软关系来表达对象之间的对应关系，以解决不确定性问题，并给出了一个示例来说明如何将该算法应用于不确定性问题。