Research on the Intelligent Screening Algorithm for College Faculty Recruitment Based on BiGRU-attention

With the rapid development of higher education, university faculty recruitment is facing increasing pressure to resume screening. Traditional screening methods are inefficient and highly subjective, making it difficult to meet the needs of universities for outstanding talent. This study aimed to construct an intelligent screening model based on the Bidirectional Gated Recurrent Unit (BiGRU) and attention mechanism to improve the efficiency and accuracy of university faculty recruitment. First, a large amount of university faculty recruitment resume data were collected and preprocessed to construct a high-quality dataset. Subsequently, the BiGRU model was introduced to deeply mine the text features of the resumes. By taking advantage of its ability to effectively process sequential data and capture contextual information, the model enhances the ability to extract key information from resumes. Simultaneously, combined with the attention mechanism, the model could focus on important features, further improving the screening accuracy. The experimental results showed that the constructed BiGRU-attention model performs excellently in the task of screening university faculty recruitment resumes. Compared with traditional methods, it significantly improved indicators such as the accuracy and recall rate. It could provide more efficient and intelligent decision support for university recruitment work, help universities select outstanding teaching talents that better meet job requirements, and promote the construction of teaching staff in higher education.