Data Mining Algorithm for Web Learning Resource Information Flow Loss Based on Weighted Depth Forest

Abstract

When processing the lost data of web learning resource information flow, the noise in the data signal cannot be eliminated, resulting in inaccurate detection of the lost data of web learning resource information flow in the later stage. Therefore, a data mining algorithm is proposed based on weighted depth forest for web learning resource information flow loss. Based on building a brand-driven Web data acquisition model to collect data, this method uses clustering analysis technology to extract the lost data feature information of web learning resource information flow. It carries out wavelet threshold denoising on it. According to the characteristics of lost data, the lost data mining of web learning resource information flow is completed. Experimental results show that the proposed algorithm has a low error rate, high accuracy, high labour intensity, high efficiency and high performance.