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A recommendation method that enhances user behavior features using a large language model generates a semantic representation embedding vector for each item based on its name, category, and description, thus creating a semantic vector library for the items. A training sample library is constructed based on the dataset records. Each training sample includes user features, target item features, user's historical item interaction sequence features, and a label indicating whether there was an interaction. After constructing and training the recommendation model using the semantic vector library and the sample training library with a two-layer filtering approach, real-time recommendations are made online. This invention generates semantic representation embedding vectors for items by collecting their titles, descriptions, and reviews. By filtering and enhancing the sequence features of items based on the similarity between semantic vectors, the accuracy of the recommendation model's predictions is improved.