In the rapidly evolving field of scientific research, efficiently extracting key information from the burgeoning volume of scientific papers remains a formidable challenge. This paper introduces an innovative framework designed to automate the extraction of vital data from scientific PDF documents, enabling researchers to discern future research trajectories more readily. AutoIE uniquely integrates four novel components: (1) A multi-semantic feature fusion-based approach for PDF document layout analysis; (2) Advanced functional block recognition in scientific texts; (3) A synergistic technique for extracting and correlating information on molecular sieve synthesis; (4) An online learning paradigm tailored for molecular sieve literature. Our SBERT model achieves high Marco F1 scores of 87.19 and 89.65 on CoNLL04 and ADE datasets. In addition, a practical application of AutoIE in the petrochemical molecular sieve synthesis domain demonstrates its efficacy, evidenced by an impressive 78\% accuracy rate. This research paves the way for enhanced data management and interpretation in molecular sieve synthesis. It is a valuable asset for seasoned experts and newcomers in this specialized field.

翻译：在快速发展的科学研究领域中，如何从迅速增长的科学论文中高效提取关键信息仍是一项严峻挑战。本文提出了一种创新框架，旨在自动化地从科学PDF文档中提取关键数据，使研究人员能够更便捷地洞察未来研究趋势。AutoIE独创性地整合了四个新型组件：（1）基于多语义特征融合的PDF文档布局分析方法；（2）科学文本中的高级功能模块识别技术；（3）用于分子筛合成信息提取与关联的协同技术；（4）专为分子筛文献定制的在线学习范式。我们的SBERT模型在CoNLL04和ADE数据集上分别实现了87.19和89.65的宏F1高分。此外，AutoIE在石化分子筛合成领域的实际应用验证了其有效性，准确率高达78%。这项研究为分子筛合成领域的数据管理与解析开辟了新路径，无论是资深专家还是该专业领域的新手，都能从中获得宝贵支持。