Design of intelligent employment recommendation system for agricultural industry based on twin network and BERT

Agriculture is a traditional pillar industry in China, and the precision of intelligent employment in the agricultural sector. Firstly, the system combines dual network BERT, a recommendation system construction method that uses dual network BERT (bidirectional encoder representation from transformer), which can be used for job description functions in agricultural systems. In addition, dual network BERT processing of text information and recommendation systems can promote the efficient development of this industry. This article proposes to improve the matching achieved by staff and simulate the interaction between job seekers and positions. The proposed recommendation system can better understand the background of job seekers while meeting the specific needs of the agricultural industry. The advantages of relationship modeling, surface testing, and referencing BERT's previous understanding of natural language processing, the geographic positioning assessment emphasizes the integration of advanced artificial intelligence technology to optimize agricultural sector relationships and references BERT's previous understanding of natural language processing. It takes into account the advantages of fully modeling skills and referencing BERT's previous understanding of natural language processing, highlighting the potential of human resource management and industry specific knowledge factors to improve the accuracy of job recommendations.