Existing metrics for evaluating complex ontology matching systems often fail to adequately capture the intricacies of (m:n) correspondences. This limitation results in partial or biased alignment quality assessments. This paper introduces a novel metric specifically tailored for complex ontology matching, extending traditional evaluation frameworks by incorporating subgraph similarity measures to ensure structural consistency with reference alignments. It utilizes a tree similarity-based approach, ensuring robustness against common issues such as order variance and detecting incorrect correspondences while adhering to key evaluation properties like completeness and correctness. Empirical experiments conducted on the OAEI complex track datasets demonstrate the superior adaptability of the metric in distinguishing correct structural correspondences compared to conventional and instance-based evaluation methods.