[研究目的]数据异构性阻碍了大数据集成分析,而异构数据的深度融合学习能够增强学科数据分析能力,为预见学科新兴主题提供有力支撑.[研究方法]探测分析由两部分衔接构成,一是实现多元异构学科数据深度融合的图卷积神经网络(GCN),二是旨在学科主题预测的LSTM模型.具体地,通过GCN的深度学习能力,将包含多维特征和共现关系的异构主题数据转化为同构表示向量,不但实现异构融合,更为后续预测模型提供统一数据基础;然后,将主题表示向量时间序列输入LSTM模型,预测学科主题的新兴特征,为前瞻预见学科新兴主题提供决策支持.[研究结论]以图书情报学为对象的实证充分检验了GCN+LSTM的设计合理性,融合模型比非融合模型在主题趋势预测中展现出明显优势.
[Research purpose]Data heterogeneity makes large data integration analysis difficult.Deep fusion learning for data with vari-ous structures aids in improving academic data analysis capability,and support the prediction of scientific emergency topics.[Research method]Two components make up detection analysis:(1)Graph Convolution Network(GCN)for deep fusion with various and hetero-geneous academic data.(2)LSTM model for topic prediction in academic fields.In particular,using deep learning capability of GCN,heterogeneous topics data,including multi-characteristics and co-occurrence relations,are transformed into homogeneous representation vectors,realizing heterogeneous fusion while also providing a unified data base for the subsequent prediction model.In order to anticipate the emergency characteristics of academic topics and provide decision assistance for predicting academic emergency topics,topic represen-tation vectors are then fed into a LSTM model to predict academic emergency characteristics,giving decision assistance for predicting aca-demic emergency topics.[Research conclusion]In the academic discipline of library and information science,the empirical findings sup-port the design of GCN+LSTM model as being reasonable.In addition,the fusion model outperformed than non-fusion models.