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By introducing beetle antennae search (BAS), the paper optimized recommendation results in a multidimensional feature space, taking into account user satisfaction, cultural adaptability and resource diversity, and improving the relevance and personalization of recommendations. It generated a preliminary recommendation list through a hybrid recommendation model, integrating user historical behavior and content feature information to achieve personalized recommendations. The BAS algorithm is introduced to further optimize the preliminary recommendation list. The fitness function takes into account user satisfaction, cultural adaptability and diversity. The weights of user-based collaborative filtering and content-based recommendation are 0.6 and 0.4, respectively. The mixed recommendation is better than the single recommendation. In the Top-3 recommendation, the accuracy of the mixed recommendation reaches 93.4%. When the weight parameters of user satisfaction, cultural adaptability and diversity are 0.5, 0.3 and 0.2, the accuracy of the system reaches 97.8%. BAS, genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) are optimized and compared. For six different cultures, BAS has the highest resource recommendation accuracy. In European culture, the recommendation results optimized by BAS achieved a coverage rate of 97.4% and a user retention rate of 93.5%. This paper can effectively improve the recommendation effect of cross-cultural English teaching resources by combining hybrid recommendation and longhorn beard search algorithm.<\/jats:p>","DOI":"10.1142\/s0218126625504663","type":"journal-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T10:29:20Z","timestamp":1755858560000},"source":"Crossref","is-referenced-by-count":0,"title":["Cross-Cultural English Learning Resource Recommendation System Using Beetle Antennae Search Algorithm"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6793-3942","authenticated-orcid":false,"given":"Lili","family":"Zhao","sequence":"first","affiliation":[{"name":"Bengbu University, Bengbu, Anhui 233030, P. R. 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