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Mgmt."],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:p>In today\u2019s rapid development of global technology, the global demand for scientific and technological talents remains high. To provide a reliable talent referral channel, a talent recommendation model based on Bidirectional Encoder Representation from Transformers (BERT) and Bi-directional Long Short-Term Memory (BLSTM) was constructed. This model enables the matching of talented individuals with job opportunities. The results demonstrated that the accuracy and F1 value of BLSTM-BERT in the test set were 0.95 and 0.92, respectively. The precision rate, recall rate, F1-socre value and accuracy rate of BLSTM-CNN model were 0.96, 0.97, 0.96 and 0.97, respectively. The correct prediction rate of the talent recommendation model for the four types of talents was 1.0. 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