English children’s literature has strong application value in educational content selection. This study takes classic English children’s literature texts as the research object, and constructs a semantic theme mining model based on the implicit Delicacy Distribution (LDA). Through keyword weight analysis and theme probability distribution calculation, multi-dimensional theme clustering and visual characterization of literary works are realized. According to the 2378 English children’s literature collected in the corpus, the LDA model was used to extract five core themes: “Adventure and Fantasy”, “Friendship and Teamwork”, “Growth and Self-Identity”, “Family and Affection”, and “Nature and Animals”.A semester-long controlled experiment was conducted with third-grade students in an elementary school in Guangdong Province, designing graded English teaching content based on the results of topic distribution. Through the questionnaire survey, vocabulary test and reading ability assessment, it was found that students in the experimental group significantly outperformed the control group in terms of active interest in learning (12.42% increase in mean value) and independent learning ability (15.67% increase in test scores) (p<0.05). The study shows that the educational content adaptation method based on the LDA theme model can effectively optimize the selection strategy of teaching resources, and provide a theoretical basis and practical path for the precise matching of literary themes and cognitive development stages in children's English teaching.