  The recognition of abstracts is crucial for effectively locating the content
and clarifying the article. Existing move recognition algorithms lack the
ability to learn word position information to obtain contextual semantics. This
paper proposes a novel enhanced move recognition algorithm with an improved
pre-trained model and a gated network with attention mechanism for unstructured
abstracts of Chinese scientific and technological papers. The proposed
algorithm first performs summary data segmentation and vocabulary training. The
EP-ERNIE$\_$AT-GRU framework is leveraged to incorporate word positional
information, facilitating deep semantic learning and targeted feature
extraction. Experimental results demonstrate that the proposed algorithm
achieves 13.37$\%$ higher accuracy on the split dataset than on the original
dataset and a 7.55$\%$ improvement in accuracy over the basic comparison model.
