Time Sequence Based Dynamic Hirsch Index Measure for Scholar Impact Factor

The Hirsch Index (H-index) has become a widely used index for evaluating the academic influence of scholars, which is of great significance for talent evalu-ation and resource allocation. Currently, the measure of the H-index is mainly based on some static and present-day academic features of scholars using dif-ferent combinations of features. To gain a more comprehensive understanding of scholars' academic trajectories, it is necessary to consider their historical H-index for the future scholar impact factor. In this paper, we introduce the time sequence into the H-index and analyze the dynamic trend of the H-index of scholar’s influence. Through the comparison of linear regression model, dy-namic Bayesian networks (DBNs), and the sequence-to-sequence model of LSTM, the experimental results show that the LSTM model is the most effec-tive for short-term H-index prediction. It achieves an R2 exceeding 0.95, which surpasses the linear regression model and DBNs by 11% and 8%, respectively. Additionally, the LSTM model exhibits a significantly lower MAE of only 1.30, representing a decrease of 1.0 and 0.9 compared to the linear regression model and DBNs, respectively. But for a long-term prediction, the perfor-mance of the LSTM model becomes worse and the DBNs exhibits better per-formance. Our method can effectively predict the H-index on Chinese medi-cine scholar data and avoids the problem of feature collection compared with the traditional method based on static academic features.