为了准确、有效地识别学科研究热点与前沿主题，文章选择Altmetric.com平台17个指标加权综合的Altmetrics Attention Score（以下简称AAS）值，基于z指数方法识别高关注度研究主题。选择SSCI数据库中情报学5种优秀期刊2018年的论文为样本，获取这些论文的AAS值，将AAS值替代z指数中的被引频次，构建zt指数模型识别高AAS关注度研究主题，构建平均数基准线分类模型对识别结果进行细分。识别出情报学的16个高关注度研究主题，并细分潜力类、突现类、核心类、边缘类4类学科研究内容，发现本文构建的zt指数模型和高关注度学科研究主题分类模型是可行、有效的。

In order to identify subject research hotspots and cutting-edge topics accurately and effectively, choosing the Altmetrics Attention Score(AAS) value of the Altmetric.com platform, which is weighted synthesis of 17 indicators, to identify high-profile research topics based on the z-index. Articles of five excellent information science journals in 2018 selected from SSCI database as samples to obtain the AAS values of these papers. The citation frequencies in the z-index are replaced by AAS, and a zt index model was constructed to identify the research topics. The authors construct zt index model to identify high AAS attention research topics, and construct a mean baseline classification model to subdivide the recognition results. There are 16 high-interest research topics of information science were identified, and the research contents of four categories of potential, emerged, core, and marginal categories were subdivided. Research found that the zt index model and the topic classification model of high-concerned subject research constructed
in this paper are feasible and effective.