Effective Information Extraction Algorithm of Dynamic Logistics Big Data Based on SVM

Abstract

With the advent of the era of big data, data plays a more and more important role. At the same time, the massive data also contains a variety of invalid information. The existing data information extraction methods do not perform well in dealing with this kind of data, which makes the results deviate greatly. To solve these problems, this paper proposes an efficient information extraction algorithm based on SVM for dynamic logistics big data. First of all, considering the unstructured characteristics of logistics dynamic big data, preprocessing data, including cleaning data, data specification and transformation, as well as data dimensionality reduction, to facilitate subsequent operations. Then, the approximate support vector regression function is used to mine the information features effectively. Through the simulation experiment, the results show that the accuracy of the proposed algorithm is about 82.3%, which is significantly higher than the other two algorithms. This shows that the method proposed in this paper has obvious effect on the effective information extraction of logistics big data.