Research on the application of enterprise big data mining based on deep neural network

Shaoxi Li

doi:10.1117/12.3008940

27 October 2023 Research on the application of enterprise big data mining based on deep neural network

Shaoxi Li

Proceedings Volume 12922, Third International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2023); 129222O (2023) https://doi.org/10.1117/12.3008940
Event: The Third International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2023), 2023, Xiamen, China

Abstract

With the rapid development of the Internet, the health big data industry is highly valued by society and the state, and how to effectively mine the large amount of health data generated by medical enterprises has become a current research hotspot. Neural networks, as a new network technology, can be well combined with health big data, and thus promote the deep mining of health big data. Therefore, based on the analysis of the characteristics of Convolutional Neural Networks (CNN), the study combines it with the Apriori algorithm and constructs a CNN-Apriori based enterprise big data mining model. The results show that the CNN-Apriori model takes 0.813s to converge to a steady state and has a fitting accuracy of 96.012% at a running time of 0.900s. In the mining of factors related to users' physical fitness, the users' physical fitness score improved most significantly from 90.023 to 96.321, and all the users' physical fitness scores obtained by the method improved to above 90. This shows that the proposed CNN-Apriori model can effectively mine the factors related to users' physical quality, which has good application effect in the big data mining of medical enterprises and has certain promotion value.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shaoxi Li "Research on the application of enterprise big data mining based on deep neural network", Proc. SPIE 12922, Third International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2023), 129222O (27 October 2023); https://doi.org/10.1117/12.3008940

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