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.

通过改进的预训练模型和带有注意机制的门控网络，提出了一种新颖的增强型移动识别算法，用于处理中文科技论文的非结构化摘要，该算法能够学习单词位置信息，实现深度语义学习和目标特征提取，实验证明其在分离数据集上的准确率较原始数据集提高了13.37%，比基本比较模型提高了7.55%。

基于预训练模型和位置嵌入的新型运动识别算法