This invention discloses an AI-based method for identifying and personally recommending children's reading interests, specifically relating to the fields of
data processing and intelligent recommendation technology. The method includes: collecting
multimodal data from children's reading and performing spatiotemporal alignment preprocessing; using a
deep learning model to perform multimodal fusion identification of emotional pleasure, focus index, and interest focus; constructing a
knowledge graph of reading materials and a graph of children's developmental stages; performing similarity retrieval based on interest focus; calculating the "zone of proximal development deviation value" to filter a preliminary recommendation
list; using interactive trial reading
verification; and generating a final personalized recommendation
list. This invention accurately captures children's latent interests through multimodal emotion computing, combines dual knowledge graphs to match
cognitive development stages, and forms a
closed loop through interactive
verification. It solves the technical problem that traditional recommendation methods are unable to adapt to the dynamic reading needs of young children, significantly improving the accuracy and
personalization of recommendations.