Prediction of Research Hotspots Based on LSTM: Taking Information Science as Example

Detection and identification become the prediction of future research hotspots in the discipline, which is important to grasp the current status and development trend of the discipline research. In this paper, we use the cumulative topic heat model to calculate the research heat of each research topic in intelligence science from 2000 to 2020 and use the first 70% of the data as the training set, use the LSTM model for prediction, and construct the ECM model for error correction. The actual topic hotness of intelligence science for the latter 30% of data was used as the validation set to verify the effectiveness of the method. It was found that the average deviation rate of the method's prediction results fluctuated between 9.75% and 12.68%, and the average number of error entries was about 0.161, which had high validity. The study also predicts that by 2025, topics such as “crisis warning” and “health information services” in intelligence will continue to rise in popularity and “scientific data” and “data mining” will continue to rise in popularity. The hotness of “data mining” will remain stable, while the hotness of “citation analysis” and “ontology” will gradually decline.