Transknowletology (a theory defining translation as the global reproduction of local knowledge) reveals a deep-seated dilemma when applied to machine translation of peony cultural images: what machines produce is not “knowledge reconstruction” but “knowledge flattening”—the multilayered local knowledge is reduced to one-dimensional universal concepts. Through a comparative analysis of machine translations (including traditional NMT and generative AI) and human translations of three text types—classical poetry, botanical records, and tourism promotions—this study systematically diagnoses three interconnected failures in handling peony-related cultural knowledge: misidentification of knowledge, loss of contextual knowledge, and failure of pragmatic knowledge. It further reveals the nature of generative AI: statistical imitation of knowledge rather than rational cultural judgment. In response, this study constructs a post-editing intervention model based on the knowledge operation process, advancing the PE paradigm from “correction-patching” to “evaluation-optimization” through three strategies: precision of knowledge units, compensation of contextual knowledge, and adaptation of pragmatic knowledge. This research not only offers a theoretical framework and operational path for the accurate translation of Chinese cultural knowledge in the AI era, but also enriches the understanding of “knowledge reconstruction” in Transknowletology with the concept of “knowledge flattening,” revealing the non-negotiable “human judgment” in human-machine collaboration.