The high-level paper is one of the symbolic achievements of excellent scientific talents. Focusing on the "Web of Science (WOS)" hot research disciplines, on the basis of constructing the Neo4j semantic network graph of academic papers and mining active scientific research communities, the PageRank-based talent mining algorithm was used to realize the mining of outstanding scientific research talents in the scientific research communities. Firstly, the existing talent mining algorithms were studied and analyzed in detail. Secondly, combined with the WOS data, the PageRank-based talent mining algorithm was optimized and implemented by adding consideration factors such as the paper publication time factor, the author's order descending model, the influence of surrounding author nodes on this node, the number of citations of the paper. Finally, experiments and verifications were carried out based on the paper data of the communities of the hot discipline computer science in the past five years. The results show that community-based mining is more targeted, and can quickly find representative excellent and potential talents in various disciplines, and the improved algorithm is more effective and objective.

To improve the training efficiency of Motor Imagery (MI) under visual guidance and the classification accuracy of Brain-Computer Interface (BCI), the influence of Virtual Reality (VR) environment on MI training and the differences of ElectroEncephaloGram (EEG) classification models under different visual guidance were studied. Firstly, three kinds of 3D hand interactive animation and EEG acquisition program were designed. Secondly, in the rendering environment of Helmet-Mounted Display (HMD) and planar Liquid Crystal Display (LCD), the left hand and right hand MI training was conducted on 5 healthy subjects, including standard experiment (the single experiment lasted for 5min) and long-time experiment (the single experiment lasted for 15min). Finally, through the pattern classification of EEG data, the influence of rendering environment and content form on classification accuracy was analyzed. The experimental results show that there is a significant difference in the presentation of HMD and LCD in visual guided MI training. The VR environment presented by HMD can improve the accuracy of MI classification and prolong the duration of single training. In addition, the classification model under different visual guidance content is also different. When the testing samples and training samples have the same visual guidance content, the average classification accuracy is 16.34% higher than that of different samples.

Classic decision tree algorithm is unfit to cope with uncertain data pervaded at both the construction and classification phase. In order to overcome these limitations， D-S decision tree classification algorithm was proposed. This algorithm extended the decision tree technique to an uncertain environment. To avoid the combinatorial explosion that would result from tree construction phase, uncertainty measure operator and aggregation combination operator were introduced. This D-S decision tree is a new classification method applied to uncertain data and shows good performance and can efficiently avoid combinatorial explosion.