[Objective] Multi-energy microgrids（MEMGs）can integrate multiple energy carriers to improve energy efficiency，thereby contributing to the achievement of "dual carbon" goals. [Methods] This study proposes a data-driven distributionally robust optimization scheduling method for MEMGs that considers uncertainty correlations and outlier data. First，an ambiguity set incorporating uncertainty correlations is introduced using the copula function. A distributionally robust optimization scheduling model with opportunity constraints is then formulated，integrating the ambiguity set and opportunity constraints to address the uncertainty correlations. Second，because the distributionally robust optimization model cannot be solved directly，a worst-case transformation method is derived for the proposed ambiguity set using dual theory，McCormick relaxation，and conditional value-at-risk approximation. This transforms the distributionally robust model into a linear deterministic model，thereby enabling an efficient solution using optimization solvers. Finally，a sample-pruning algorithm is proposed，which iteratively generates subsamples from the original dataset by removing the outliers and extreme data points. This approach mitigates the adverse effects of such data on distributionally robust opportunity constraint scheduling results. [Results] Case simulations demonstrate that the proposed distributionally robust model effectively eliminates unrealistic distributions in the ambiguity set，resulting in an 8.16% reduction in out-of-sample costs. The proposed sample-pruning algorithm further reduces the out-of-sample costs by 3.33%. [Conclusions] The proposed method enhances the scheduling efficiency and ensures reliability，which collectively demonstrates its superiority.