The rapid expansion of the health preservation market has been accompanied by widespread misinformation

vague efficacy claims

insufficient evidence support

and inadequate standardization in the medicinal and edible homologous (MEH) industry. To address these challenges

this study developed an AI - assisted verification system that integrates multi - source data standardization

deep learning - based identification

and lightweight Web application deployment.A unified data specification covering 100 representative MEH materials was established by synthesizing traditional Chinese medicine (TCM) classics

modern clinical studies

and pharmacopoeial standards

raising the data standardization rate from 62% to 95%.A hybrid deep learning model incorporating a convo

lutional neural network and attention mechanism was constructed for component recognition

efficacy matching

and contraindication warning. Independent testing showed that the model achieved 91.0% accuracy in component identification

[

1

1

]

93.0% accuracy in efficacy matching

and 93.0% accuracy in contraindication warning.A lightweight Web platform was further developed to enable real - time query

evidence - level annotation

and science popularization. User evaluation indicated that 87.0% of respondents recognized the practicability and reliability of the platform.This study establishes a systematic and replicable framework of "data standardization–model validation–platform deployment

" which provides a feasible technical route for the scientific verification of MEH materials and the governance of pseudoscientific health claims. The findings contribute to enhancing transparency and standardization in the MEH industry and offer a scalable

lightweight innovation paradigm for digital TCM and health informatics research.