Enhancing Research Support for Humanities PhD Teachers: A Novel Model Combining BERT and Reinforcement Learning

Newly established undergraduate institutions face unique challenges in supporting the research efforts of PhD teachers in the humanities, who often encounter difficulties such as limited funding, scarce resources, publication bias, collaboration obstacles, and methodological complexities. Existing support systems are inadequate in effectively addressing these diverse challenges, lacking the precision and adaptability required to provide targeted solutions. To overcome these limitations, we propose a novel deep learning-based model that integrates BERT, Recurrent Neural Networks (RNN), and reinforcement learning to systematically analyze academic texts, identify specific research difficulties, and recommend tailored breakthrough strategies. Experimental results indicate that our model achieves an F1-score of 0.87 and a precision of 0.85 in accurately detecting research challenges, while improving the consistency score of the recommended strategies by 15% compared to baseline methods. These findings highlight the model’s potential to enhance research output and collaboration efficiency among PhD teachers in the humanities, offering a solid foundation for developing intelligent support systems that better address the unique research needs of faculty in newly established undergraduate institutions.