The widespread use of AI in various industries has been facilitated by advancements in machine learning and neural networks. To shed light on the workings of opaque data-driven algorithms, several mathematical methods have emerged, such as the Shapley value, tree models, and Taylor expansion. Among these, the Shapley value stands out as a popular perturbation method, garnering significant attention. While calculating Shapley values is known to be an NP-hard problem, some researchers have introduced approximate techniques to alleviate this challenge. However, striking a balance between accuracy and time cost remains difficult, particularly as the number of players involved increases. In this paper, we propose a novel approach that efficiently computes Shapley values using fewer high-quality coalition samples, relying on the relationship map.