The Establishment of University Teachers' Knowledge Connotation Reconstruction Model Based on Big Data

摘要

To improve the knowledge management and evaluation ability of college teachers, in this paper, the reconstruction of college teachers' knowledge connotation information based on big data, and a method based on Lyapunov exponent spectrum feature extraction for college teachers' knowledge connotation information reconstruction under the big data environment is proposed. The feature analysis method is used to reconstruct the phase space of college teachers' knowledge connotation information. In the reconstructed phase space, the grid region clustering algorithm is used to carry out the fuzzy clustering processing of college teachers' knowledge connotation information, and the Lyapunov exponent spectrum is extracted from the cluster knowledge connotation information. The feature is to use the particle swarm filtering method to suppress the false data interference and realize the optimization of the knowledge content information of college teachers in the big data environment. The simulation results show that the method can be used to reconstruct the knowledge content of college teachers under the big data environment. The university students' knowledge connotation information big data regular knowledge, the knowledge connotation reconstruction based on spectral feature quantity analysis, the data clustering is better, and the accurate mining performance of college teachers' knowledge connotation big data is improved.