Aiming at the problems of content homogeneity, semantic deviation and insufficient cross-cultural adaptation in the external communication of traditional Chinese culture, this paper constructs an integrated research framework for communication content recognition, vector modeling and optimal output. This paper uses text embedding, semantic similarity calculation, topic clustering, cross-language semantic mapping and other methods to establish a multi-level representation system of word level, sentence level and topic level cooperation, and designs the mechanism of content screening, semantic matching, topic aggregation and expression optimization. The results show that the clustering compactness of the art texts of intangible cultural heritage reaches 0.88, and the inter-class separation of the classic thought texts reaches 0.84. The semantic consistency of the optimized text was improved from 0.74 to 0.87, the communication clarity was improved from 0.69 to 0.83, and the cross-cultural fitness was improved from 0.66 to 0.81. The semantic recognition accuracy, content recommendation accuracy and comprehensive performance score of the proposed method reach 0.89, 0.86 and 0.87, respectively, which are better than those of keyword matching, rule method and general text classification method. The research shows that the vector algorithm can effectively improve the semantic organization ability, expression stability and international communication adaptation level of traditional cultural communication content.
