For general process of crisis management study, researchers usually conduct a literature review attempting to integrate sound theories from the domain information of research topics. With the advance of online literature archives, researchers can facilitate their research projects in an efficient way which a traditional library is unable to competitively support it today. The published literatures characterize with a sequential stream of textual materials, which raises unique research direction for temporal literature mining techniques. Particularly, in the age of Big Data, such temporal pattern has been identified comprising valuable information in the text mining domain, which potentially reveals the evolution and transition of research trends in scientific study. In this research, we present a novel temporal literature mining method called TLTD which makes use of aging theory and integrated with lexical knowledge for modeling the semantic topics from literature text stream. Our approach aims to identify the research topics through the timeline and explore research trend from multiple academic journals. By chronologically extracting the silent topics from the transition of research publications, we can uncover the temporal topics and track the topic evolvement. Also, information related to temporal pattern and semantic feature are used to retrieve the variation of prominent topics from cross-domain databases and it enables the facilitation of the preliminary research task as conducting literature survey process. Through empirical evaluation, the result of our method demonstrates that the performance is enhanced.