Mainstream automatic evaluation metrics of machine translation typically treat all input sentences equally, overlooking the variation in translation difficulty posed by different source sentences. This often leads to inadequate reflection of system performance on structurally complex or lexically rare content. To address this limitation, we propose a novel evaluation approach TD_MT that integrates translation difficulty as a core factor in the scoring process. By analyzing representative samples using large language models and extracting syntactic and lexical features, such as dependency tree depth and word frequency, a linguistically interpretable, rule-based framework for translation difficulty classification is constructed. Evaluation scores are dynamically adjusted based on the estimated difficulty levels of source sentences, enabling a more nuanced assessment. At the sentence level, the proposed difficulty-aware machine translation evaluation metric exceeds the best-performing participating metric, XCOMET-Ensemble, by 0.004. At the system level, it exceeds XCOMET-Ensemble by 0.060. Experimental results on the WMT’23 benchmark dataset demonstrate that, compared with classical evaluation metrics and XCOMET-Ensemble, the proposed method achieves a significantly higher correlation with human judgments.