Research on Multi-Source Heterogeneous Big Data Fusion Method Based on Feature Level
Yanyan Chen, Chenxi Wang, Yuchen Zhou, Yuhang Zuo, Zixuan Yang, Hui Li, Juan Yang
Abstract
With the development of research on multi-modal data fusion and its combination with online data management, the application of multi-modal big data fusion in information management systems is more and more extensive. How to integrate multi-modal big data effectively is the key technology to building an efficient information management system. In this paper, based on the combination of a multi-support vector machine and convolution neural network, the feature-level data fusion of multi-source heterogeneous big data is implemented, and it is applied to the real data set to test the relevant model. Experimental results show that this method can not only realize heterogeneous integration of big data, but also has high accuracy and reliability.