This paper explores how artificial intelligence is transforming engagement with cultural heritage from static preservation toward interactive knowledge production. Drawing on media-historical perspectives, it proposes the concept of knowledge liberation to interpret successive stages in the evolution of knowledge environments, from oral transmission and print culture to digital networks and AI-mediated interaction. Within this framework, cultural knowledge becomes progressively less constrained by the material conditions of its transmission. The paper examines several AI-enabled platforms developed at Peking University that support large-scale digitisation, structured data extraction, knowledge-graph construction, and multimodal cultural content generation. It argues that AI is emerging as a new knowledge medium that reshapes research methodologies, expands modes of cultural representation, and strengthens connections between humanities scholarship and public knowledge production.

Large language models (llms) have made language technologies widely accessible to humanities scholars, but they have also intensified concerns about transparency, reproducibility, and interpretive responsibility. This article argues that graph-based meaning representations, especially Abstract Meaning Representation (amr) and Uniform Meaning Representation (umr), can function as interpretive infrastructure for humanities research in the age of llms. Meaning graphs are “thin” by design in that they deliberately encode a constrained set of semantic distinctions. That selectivity is not a weakness for humanistic inquiry; rather, it enables a disciplined workflow in which researchers can separate (i) the semantic commitments that a text licenses (events, participants, temporal and modal dependencies) from (ii) richer interpretive claims (stance, ideology, affect, narrative framing) that can be layered on top. I review amr and umr at a level accessible to humanities audiences, discuss what changes in the llm era (including both opportunities and limits of using llms for semantic parsing), and propose humanities-centered workflows and research questions. Several compact sample analyses illustrate how meaning graphs can support interpretive tasks in historiography, narrative analysis, and translation studies. A final section explicitly lists resources (datasets, tools, and guidelines) to support reproducible experimentation.

This article examines the methodological transformation that AI’s entry into literary criticism has set in motion. This transformation proceeds along four interrelated dimensions—technological capacity, critical method, literary ontology, and paradigm formation—that do not unfold in linear sequence but are mutually constitutive. At the technological level, the shift from machine reading to AI reading has given literary studies a new capacity for large-scale semantic analysis, though a qualitative gap persists between AI “reading” and human reading. At the methodological level, computational analysis has expanded the scale and verifiability of criticism without being equivalent to “objectivity”; its value lies in rendering the research process more transparent and reproducible. At the ontological level, generative AI poses substantive challenges to such core categories as “author,” “text,” and “literariness,” yet this disruption extends, rather than originates, the destabilizing work already undertaken by twentieth-century literary theory. Building on these three interconnected transformations, the article proposes the paradigm of computational literary criticism,” distinguishing it from Franco Moretti’s “distant reading,” Matthew Jockers’s “macroanalysis, and the broader field of digital humanities. The article argues that the core value of computational literary criticism lies not in replacing interpretation with computation, but in constructing a collaborative framework of sustained interaction between computational discovery and humanistic interpretation—a framework whose viability depends on methodological self-discipline, data ethical awareness, and an unwavering attentiveness to the humanistic core of literary inquiry.