Abstract
Exploring and predicting the hot scientific researching issues have been a focus among recent sci-tech information research. In recent years, scholars and academic literature are increasing steadily in number. It is hard to artificially track and deal with developing trends of scientific researching hotspot. The most commonly used methods in the past are simple statistical ways for keywords and high term frequency. Most of these methods spend a lot of time and manpower, and ignore the relevance between different words. In the paper we use RNN method to design our prediction and push system, which could perceive the potential researching hotspot for some time to come. The hot issues are generated based on the relevance among sci-tech words. It finds the relevant technical application to hotspots and recommends them to academic researchers. The extensive experiments demonstrate that our proposed approach has higher accuracy rate and lower false positive ratio. It can do better forward looking forecasts than word frequency statistics as well.
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