Balanced Distribution of Online Teaching Resources for Financial Management Specialty Based on Content Clustering

Abstract

In order to solve the problems of high packet loss rate and low throughput caused by the delay of traditional methods proposed at present, a content clustering based balanced allocation method for online teaching resources of financial management specialty is proposed. Calculate the information entropy of online teaching resources of financial management specialty in each dimension, and determine the content clustering feature subspace. The robust low rank constraint model is used to suppress noise and construct the multi kernel subspace clustering objective function. According to the screening results of the data clustering center of the improved k-means algorithm, the online teaching resources are clustered. Calculate the time slot window interval, and realize the real-time transmission of teaching resources through the online teaching channel. According to the overall framework of online teaching system for financial management specialty, calculate the weight distribution and spatial clustering similarity in any two clustering subspaces, determine the scheduling efficiency of user groups on each link in the order of maximum to minimum, and realize the balanced allocation of online teaching resources. Experimental results show that compared with the existing methods, the proposed method has the highest packet loss rate of 12%, the lowest delay of 10ns, and the maximum throughput of 1163b/s, respectively, which can achieve the purpose of balanced resource allocation.