Effective Information Filtering Mining of Internet of Brain Things Based on Support Vector Machine

The traditional Internet of Brain Things’ big data information filtering method ignores the extraction of big data features, the filtering effect, and the effect of denoising processing on the simulation results, resulting in low filtering accuracy and poor performance. An effective information filtering and mining algorithm for the Internet of Brain Things based on support vector machine (SVM) is proposed. First, the model construction and feature extraction of the Internet of Brain Things’ big data system are carried out. The correlation feature extraction is performed on the effective information features; the correlation factors of the effective information data are sorted; the main feature quantity of the relevance degree is extracted; and the filter non-association is designed. The information is reasonably filtered; all data are processed, converted to the same interval for processing; data protocol is implemented; and data effective information feature mining is implemented based on the SVM algorithm. The simulation results show that the algorithm is effective for filtering big data and has high precision and superior performance, which shows good application value.