Fuzzy Integration Algorithm of Big Data in Peer-to-Peer Communication Network Based on Deep Learning

Abstract

There are many kinds of big data in peer to peer communication network, traditional big data integration algorithms ignore the classification of big data state. The location correlation between big data cannot be accurately obtained, which leads to the time-consuming process of big data integration and the low acquisition accuracy of big data location correlation. A fuzzy integration algorithm for big data in peer-to-peer communication network based on deep learning is proposed. According to the big data integration conditions of peer-to-peer communication network, the benchmark model of big data integration is constructed by combining semi-supervised deep learning. The marked sample updating model of the prior single classification fuzzy integrated data model is used to process the corresponding sample data. Combining the steady-state and dynamic residuals of the datum model, multiple information grains are obtained. Based on this, kalman filter is adopted to fuzzy fusion of big data to obtain state parameters, and multi-scale analysis is carried out to filter out big data noise. Using the obtained information grains to constrain the characteristic correlation degree of big data, the position correlation of big data is completed. Implement fuzzy integration algorithm design of big data in peer-to-peer communication network based on deep learning. In order to verify the effectiveness of the proposed algorithm, a simulation experiment is designed. Experimental results show that compared with many traditional methods, the proposed algorithm takes less time and the computational complexity of the proposed algorithm is significantly reduced. And the big data integration is more accurate and has better application effect.