This study aims to explore the application of Bayesian analysis based on neural networks and deep learning in data visualization. The research background is that with the increasing amount and complexity of data, traditional data analysis methods have been unable to meet the needs. Research methods include building neural networks and deep learning models, optimizing and improving them through Bayesian analysis, and applying them to the visualization of large-scale data sets. The results show that the neural network combined with Bayesian analysis and deep learning method can effectively improve the accuracy and efficiency of data visualization, and enhance the intuitiveness and depth of data interpretation. The significance of the research is that it provides a new solution for data visualization in the big data environment and helps to further promote the development and application of data science.
Zhang W, Migiti A, Zheng F, et al., 2021, Uyghur Speech Keyword Retrieval Based on Deep Neural Network. Computer Age, 2021(11): 21–24.
Tang JY, Zhou M, 2019, Case Analysis of Network Data Visualization Based on Big Data. Integrated Circuit Applications, 38(11): 80–81.
Shi Y, Pu X, Shen L, et al., 2021, Object Detection System Based on Convolutional Neural Networks and Keywords. Computer Knowledge and Technology: Academic Edition, 17(08): 162–164.
Ruan L, Wen SS, Niu YM, et al., 2021, Deep Neural Network Visualization Based on Explainable Basis Disassembly and Knowledge Graph. Chinese Journal of Computers, 44(09): 1786–1805.
Lou YS, Guo W, 2022, Prediction of Nuclear Mass by Bayesian Deep Neural Networks. Acta Physica Sinica, 71(10): 32–41.
Zhang W, 2021, Visual Research on Keywords of Master’s Thesis in Fine Arts. The Classic of Shanhaijing, 2021(13): 0337–0337.
Liu S, Chang J, 2019, The Concept and Application of Big Data Visualization. Information Recording Materials, 22(09): 42–44.
Cao R, Huang R, Huang X, et al., 2019, A Review of Deep Learning Research in China: Visual Analysis Based on CNKI Literature Keywords. Electronic Education in Primary and Secondary Schools, 2019(Z2): 91–94.
Cao Q, 2020, Research on Keyword Distribution Visualization Based on Long Tail Theory. Science and Technology Achievements Management and Research, 2020(06): 32–37.
Peng T, Qian H, Zhou J, 2022, Keywords and Visualizing the “Twin Engines” of Major Report Communication. Urban Party Newspaper Research, 2022(04): 61–64.
Wang K, Yu H, 2021, Patent Keyword Analysis Method Based on Bayesian Network and Factor Analysis. China Science and Technology Information, 2021(18): 23–26.
Zhang Y, 2019, Visual Analysis of Literature in Environmental Journals. Environmental Protection and Circular Economy, 41(05): 97–101.
Langenberg B, Helm LJ, Mayer A,2024, Bayesian Analysis of Multi-Factorial Experimental Designs Using SEM. Multivariate Behavioral Research, 2024: 21–22.
Huang Z, Zhou Y, Shu X, et al., 2019, Speech Enhancement Method by Combining Bayesian Estimation and Deep Neural Network. Minicomputer Systems, 40(01): 40–44.
Liu Z, Liu D, 2024, Dynamic Modulus Analysis of Resonance Fracture Layer of Old Cement Concrete Slab Based on BP Neural Network and Deep Learning Model. Highway, 2024(06): 44–51.
Zhang L, Liu X, 2024, Research on Two-Dimensional Rectangular Layout Optimization Method Based on Graph Neural Network and Deep Reinforcement Learning. Forging Equipment and Manufacturing Technology, 59(02): 117–122.
Zhao J, Zhang Y, Shi Q, et al., 2024, Automatic Pedicle Screw Planning Based on Deep Learning Neural Network Technology. China Digital Medicine, 19(04): 84–91.
Han XY, Xie MX, Yu K, et al., 2024, A Computational Power Network Resource Allocation Method Combining Graph Neural Networks and Deep Reinforcement Learning. Frontiers of Information Technology and Electronic Engineering, 25(05): 701–713.
Chen Y, Huang X, 2024, Solution of Time Disturbance Wave Equation Based on Physical Information Neural Network. Oil and Gas Geophysics Committee of the Chinese Geophysical Society, Proceedings of the 6th Annual Petroleum Geophysical Conference, 2024: 4.
Zhang W, Lin A, Yang X, et al., 2024, Fusion Deep Learning Bayesian Filtering Review. Acta Automatica Sinica, 50: 1–16.