Abstract
With the advent of the era of big data, how to quickly obtain effective information and efficiently disseminate information technology has become the most popular topic. Studies have shown that the ability of the human brain to process data and information is unmatched by machines, and the processing of graphics is tens of thousands of times faster than that of words. Based on the deep belief network (DBN) algorithm, this paper studies the technology of information visualization graphic design teaching application. Firstly, the structure of the deep belief network is analysed to explore its technical application in graphic information reconstruction. It is concluded that the DBN algorithm can be used to deal with the problems of classification, regression, dimension calculation, feature point acquisition, accuracy calculation, and so on in machine learning training. Then, the deformation technology of graphic local design is studied based on the DBN algorithm to construct the visual teaching platform and analyse the technical research results of this algorithm in information graphic design. The results show that the DBN algorithm can quickly solve the problem of processing complex features in graphics, change the local deformation design of the original graphics to form new feature point data and add it to the teaching platform, and improve the ability of model fast learning and training, optimizing the operation efficiency of the teaching platform.
Highlights
With the rapid development of big data 5G era, today’s media society has dual tasks of information transmission and processing [1]
By analysing the deep belief network (DBN) algorithm model, this paper develops a new direction of the deep learning algorithm and applies this technology to various fields [13]. e DBN algorithm can carry out preprocessing, that is, fusing feature points to improve the structure of complex data nodes and improve the accuracy and quality of data input in the model
The DBN algorithm is used to realize the research of information graphic design reconfiguration technology. e grid of the source data graph is separated by the network structure, and a certain range threshold is set for the graph pixels. e gray value of the control pixel is 255, and the unified setting greater than the pixel range is 0
Summary
With the rapid development of big data 5G era, today’s media society has dual tasks of information transmission and processing [1]. We use the deep belief algorithm (DBN) in deep learning to optimize the teaching of information visualization graphic design. By analysing the DBN algorithm model, this paper develops a new direction of the deep learning algorithm and applies this technology to various fields [13]. E first part is a brief introduction of the development of the deep belief algorithm in deep learning and the basic idea of information visualization graphic design teaching. E third part is the graphic processing technology in information graphic design by using the DBN network algorithm structure. Is paper mainly focuses on deep belief network structure technology and graphic reconstruction algorithm in the teaching platform. The research results of the deformation processing technology model of information graphics design using the DBN algorithm are analysed. The research results of the deformation processing technology model of information graphics design using the DBN algorithm are analysed. e final part is the conclusion to show some important findings and suggestions
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