This article focuses on data structuring and algorithmic classification methods in digital humanities,examining how “formalization bias” is formed,how it operates,and what consequences it produces.It first distinguishes among three levels: formal logic,formal operations,and formalization bias.Formalization bias is defined as the tendency for material attributes that are codable,comparable,and visualizable to gain sustained priority in research design,evidence construction,and interpretive judgment. The article then analyzes the formation of formalization bias from three aspects: technical entry points,methodological compatibility,and incentives in knowledge production.Drawing on cases of text analysis and spatial analysis,it further explains how formalization bias functions as a structural mechanism that affects the boundaries of research objects,the weighting of evidence,and the organization of knowledge. The study argues that examining formalization bias helps reveal the selective mechanisms and interpretive boundaries through which digital methods participate in the production of humanistic knowledge.Methodological reflection in digital humanities should therefore further clarify the conditions of production,scope of applicability,and interpretive responsibility of digital outputs through approaches such as database structures that accommodate uncertainty,reflexive metadata,and transparency about failure.