Pre-trained Chinese Natural Language Processing (NLP) tools show reduced performance when analyzing poetry compared to prose. This study investigates the discrepancies between tools trained on either Classical or Modern Chinese prose when handling Classical Chinese prose and Classical Chinese poetry. Three experiments reveal error patterns that indicate the weaker performance on Classical Chinese poemsis due to challenges identifying word boundaries. Specifically, tools trained on Classical prose struggle recognizing word boundaries within Classical poetic structures and tools trained on Modern prose have difficulty with word segmentation in both Classical Chinese genres. These findings provide valuable insights into the limitations of current NLP tools for studying Classical Chinese literature.