Despite substantial exploration of preferences and stabilities in the graph model for conflict resolution (GMCR), there are not enough data to support the existing modeling components. Moreover, multiple dimensional preference structures coexist in complex, real-world, large-scale group (LSG) conflicts. This research addresses these issues with an introduction of big data technology - initiating a big-data-based LSG-GMCR under the three-dimensional preference. This study develops an improved spectral clustering method that combines trusted degrees, sentiments, and similarities to synthesize multi-dimensional attributes, providing a new perspective and a valuable tool for the traditional group conflict analysis. Regarding the clustering algorithm results, a novel big data-based LSG-GMCR under the three-dimensional preference is established to capture and evaluate decision-makers' multi-dimensional preferences and behavioral patterns. Finally, the corresponding matrix representation addresses issues like the model's logic expression complexity and the algorithm generation challenge. The proposed matrix form enhances the model's computational efficiency and the decision support system's realizability. To demonstrate the proposed method performance, it is applied to resolve an electricity rationing conflict in Northeast China and figure out effective resolution strategies.

A three-level preference (or called strength of preference) ranking structure based on option prioritization is developed within the paradigm of the Graph Model for Conflict Resolution. In a strategic conflict, a decision maker usually controls various courses of actions which are referred to as options. An option-based preference structure could efficiently model preferences under a complex conflict situation. There are three preference representations in a graph model for simple preference (or two-level preference), including Option Weighting, Direct Ranking, and Option Prioritizing in which the Option Prioritizing approach is the most effective. Therefore, the Option Prioritizing approach is extended to three-level preference from the two levels of preference in this paper. This proposed approach is more effective and convenient for modeling preference and is easy to implement into a decision support system. A specific case study is provided to show how three-level preference is calculated using the proposed approach.