Researchon Visual Modeling Technology of 3D Mining Engineering Based on Deep Learning


  • Wenle Yu
  • Pengfei Wang
  • Wanyu Du
  • Xiaochun Shen



Deep learning, 3D mining engineering, Visual modeling technology of mining engineering.


Deep learning is a study hotspot in the domain of man-made intelligence. It is an inevitable trend to use deep learning to support the study work of man-made intelligence, and it has shown its act advantages in the domains of picture, speech and text. Interpretive approach of deep learning is an interdisciplinary study subject of man-made intelligence, machine learning(ML), cognitive psychics, logic and many other disciplines. It has vital abstract study meaning and actual apply worth in message push, medical study, finance, message security and other domains. Deep learning is a new study direction in the domain of ML. By imitating the structure of human brain, it can efficiently course complicated input data, smartly learn divers knowledge, and availably solve many kinds of complicated smart question. In recent years, with the emergence of efficient learning LRUs for deep learning, the ML community has set off an upsurge of studying the theory and apply of deep learning. The rise of 3D modeling technology has promoted the vigorous expand of computer simulation and virtual reality technology, and various 3D modeling software platforms and simulation systemics have emerged as the times require. These platforms and systemics provide powerful design tools and intellectual support. Through the simulation analysis of real scenes, people can directly start with 3D notions and ideas and make visual design schemes and evaluation systemics.


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Jürgen Schmidhuber. Deep learning in neural networks[J]. Neural Netw, Vol.42(2015)No.13,p.42-62.

Litjens G,Kooi T,Bejnordi B E,et al. A Survey on Deep Learning in Medical Image Analysis[J]. Medical Image Analysis, Vol.42(2017),No.9,p.60-88.

Yin B C,Wang W T,Wang L C.Review of Deep Learning[J]. Journal of Bjing University of Technology,Vol.41(2015)No1, 41,p.48-59.

Cohen N,Sharir O,Shashua A.On the Expressive Power of Deep Learning: A Tensor Analysis[J]. Computer ence,Vol.53(2016)No,42,p.23-65.

Marcus G.Deep Learning: A Critical Appraisal[J]. Vol.52(2018)No,24,p.432-643.

Tongtong W U,Zhou G.Research of Visual Reality 3D modeling technology[J]. Intelligent Computer and Applications, Vol.53(2016)No,4,p.52-62.

Zeng M,Li G,Zhou Q,et al. From MOOC to SPOC: Construction of a Deep Learning Model[J]. China Educational Technology, Vol,73(2015)No,42,p.63-73.

Wen C K,Shih W T,Jin S.Deep Learning for Massive MIMO CSI Feedback[J]. IEEE Wireless Communications Letters, Vol,51(2017)No,53,p.1-1.

Zhang J,Wang H,Yang G,et al. Review of deep learning[J]. Application Research of Computers, Vol,63(2018)No,53,p.5-62.

Ohsugi H,Tabuchi H,Enno H,et al. Accuracy of deep learning, a machine-learning technology, using ultra–wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment[J]. Scientific Reports,Vol,59(2017)No,7,p.9425.







How to Cite

Researchon Visual Modeling Technology of 3D Mining Engineering Based on Deep Learning. (2022). Academic Journal of Science and Technology, 2(2), 118-121.

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