Medieval Latin alchemy, still fresh literary tradition, constituted a dynamic confluence of multiple literary genres. Drawing on broader contemporary narrative traditions while simultaneously cultivated a semi-fluid genre typology shaped by utilitarianism and esoteric strategies. Narrative texts, for example, contained not only narration itself but also densely symbolic allegories and theoretical excursions, alongside practical recipes and descriptions of alchemical praxis and laboratory instruments. Within certain alchemical traditions, particularly within florilegia (excerptive anthologies), the distinction between narrative fiction and instructive recipe literature could become remarkably thin. Consequently, the tradition of alchemical writing continues to pose significant interpretative challenges for contemporary scholars.

This study draws on a corpus of over fifty digitised medieval Latin alchemical texts and proposes a taxonomy of alchemical genres through the computational analysis of their sectional composition. Supervised machine learning classification techniques, model genre differentiation based on the manually labelled textual sections. These features are based on the level of their narrativity, descriptivity and use of formal structures (e.g. recipes). Feature extraction enables the identification of recurrent compositional patterns of identified “building blocks” across texts and their variants.

Training, tuning and evaluating of classification models on annotated data allows for the systematic characterisation of literary genre categories at both the macro-structural level of Latin alchemy and in terms of intra-textual dynamics. The results illuminate strategies of textual organisation, degrees of genre stability, and the distribution of functional elements across genres of Latin alchemical genres. In addition to offering new insights at the level of textual analysis, the project contributes to the modelling of a complex literary tradition.