“…Traditionally, web data mining models in large data environment mainly adopt high order cumulant feature extraction, time-frequency analysis and feature extraction, wavelet analysis, support vector machine classification mining algorithm, and data mining algorithm based on rough set classification in large data environment; there are many drawbacks in the web data model, such as the inaccuracy of data, the lack of effectiveness [2], the error resulting in the rule pattern of the system coding, and the other most important thing is that in the process of mining the data [3], it is not possible to determine whether the system is safe or not, if the data is excavated into an unsafe system, not only the data are data, but the data are not the same in this case; in reference [4], a feature data mining algorithm is proposed based on the distributed feature partition extraction of mass web access time, and improved the web data by multi-layer autoregressive vector analysis. Classification mining ability, but the computation cost of the algorithm is large, and the time delay error occurs in web information retrieval.…”