Recent advances in computational stylometry have enabled scholars to detect authorial signals with a high degree of precision, but the focus on accuracy comes at the expense of explainability: powerful black-box models are often of little use to traditional humanistic disciplines. With this in mind, we have conducted stylometric experiments on Maospeak, a language style shaped by the writings and speeches of Mao Zedong. We measure per-token perplexity across different GPT models, compute Kullback–Leibler divergences between local and global vocabulary distributions, and train a TF-IDF classifier to examine how the modern Chinese language has been transformed to convey the tenets of Maoist doctrine. We offer a computational interpretation of ideology as reduction in perplexity and increase in systematicity of language use.