Research on the Education and Teaching of Aesthetic Education Courses in Colleges and Universities Based on Deep Learning——Taking the Basic Ballet Training Course as an Example

Xin Pan

doi:10.47297/wspiedWSP2516-250002.20240802

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Research on the Education and Teaching of Aesthetic Education Courses in Colleges and Universities Based on Deep Learning——Taking the Basic Ballet Training Course as an Example

8卷第2期

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Research on the Education and Teaching of Aesthetic Education Courses in Colleges and Universities Based on Deep Learning——Taking the Basic Ballet Training Course as an Example
Research on the Education and Teaching of Aesthetic Education Courses in Colleges and Universities Based on Deep Learning——Taking the Basic Ballet Training Course as an Example
Jiangsu College of Nursing, Huaian Jiangsu 223003, China
2024年8卷第2期页码：16-24
纸质出版日期： 2024-02-28 ，
DOI：10.47297/wspiedWSP2516-250002.20240802
Accepted：

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Research on the Education and Teaching of Aesthetic Education Courses in Colleges and Universities Based on Deep Learning——Taking the Basic Ballet Training Course as an Example[J]. 国际教育与发展（英文版）, 2024,8(2):16-24.

Xin Pan.Research on the Education and Teaching of Aesthetic Education Courses in Colleges and Universities Based on Deep Learning——Taking the Basic Ballet Training Course as an Example[J].Journal of International Education and Development,2024,08(02):16-24.
Research on the Education and Teaching of Aesthetic Education Courses in Colleges and Universities Based on Deep Learning——Taking the Basic Ballet Training Course as an Example[J]. 国际教育与发展（英文版）, 2024,8(2):16-24. DOI： 10.47297/wspiedWSP2516-250002.20240802.

Xin Pan.Research on the Education and Teaching of Aesthetic Education Courses in Colleges and Universities Based on Deep Learning——Taking the Basic Ballet Training Course as an Example[J].Journal of International Education and Development,2024,08(02):16-24. DOI： 10.47297/wspiedWSP2516-250002.20240802.

摘要

Abstract

In order to improve the teaching effect of ballet basic training course

taking ballet basic training course as an example

this paper puts forward the research on college aesthetic education course education and teaching. Deep learning. Construct the data structure model of the distribution of ballet basic course resources

use the spatial undersampling technology to sample the distribution of ballet basic course resources and conduct information fusion

extract the joint similar feature set of ballet basic course resources. The simulation results show that this method has better intelligence and better performance of course rule design

and improves the teaching level.

关键词

Keywords

Aesthetic education courses in colleges and universitiesEducational reformBasic ballet training courseTeaching model

references

LI S Y. Discussion on the teaching reform of aesthetic education in colleges and universities[J]. Hu Nan Bao Zhuang, 2020, 35(6): 140-144.

FAN H J, LIU J J, HUANG Y C, et al. Research on no-reference image blur detection[J]. Fujian Computer, 2020, 36(7): 61-63.

WANG S H, WU L R, YOU Z Q, et al. A fast ambiguity solution method for network RTK reference station[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2324-2333.

ZHU J. The research of educational reform on higher education textbook design based on concept of aesthetic education[J]. Journal of Aesthetic Education, 2020, 11(6): 34-39.

ZHANG Y P, WANG Y. On the development of ballet auxiliary training teaching in China[J]. Journal of Beijing Dance Academy, 2022, 154(2): 119-124.

JIANG W, LI Z H, ZHANG M. Syntax-enhanced UCCA semantic parsing[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2020, 56(1): 89-96.

WANG Y L. A semantic analysis of emphatic expressions of English cognate objects[J]. Journal of Henan Polytechnic University, 2020, 21(2): 58-62.

HE D P, ZHANG W Y, HUANG H. A hybrid recommendation algorithm based on multi-source information clustering and IRC-RBM[J]. Computer Engineering and Science, 2020, 42(6): 1089-1095.

MAO W. Fusion spectral clustering and multi-feature remote sensing image segmentation[J]. Software Guide, 2020, 19(3): 248-251.

XU H. Optimization from caving to filling method based on rough fuzzy set theory[J]. China Mining Magazine, 2020, 29(3): 110-114.

YANG J, HUANG M Y, CHEN J. Neighborhood-based multi-granulation rough fuzzy sets and their uncertainty measure[J]. Journal of Chongqing University of Posts and Telecommunications, 2020, 32(5): 898-908.

BAI Y, CHEN R B. Research on security analysis and processing of geographic big data based on association rules[J]. Computer Simulation, 2020, 37(9): 222-225, 327.

PAN Z H,WAN Z P, XIE H M. Task allocation algorithm in MOOC resource crowdsourcing platform combined with association rules[J]. Computer Technology and Development, 2020, 30(4): 189-194.

LIU S J, GUO Y, QU G H, et al. Study on abnormal conditions monitoring method based on trends similarity features[J]. Process Automation Instrumentation, 2020, 41(2): 37-41, 46.

YUAN Q, HUANG J H. Research on semantic feature extraction and translation algorithm based on graph regular algorithm[J]. Electronic Measurement Technology, 2020, 43(8): 91-95.

JIANG F, GU Q, HAO H Z, et al. Grain segmentation of sandstone thin section images based on semantic feature extraction[J]. Science in China(Information Sciences), 2020, 50(1): 109-127.

SHI X R, QIAN Y H, LI F J. MS2BC a multi-view space structure-based clustering algorithm[J]. Journal of Southwest University(Natural Science), 2020, 42(11): 59-67.

LIU H K, ZHANG J F. A density clustering analysis algorithm based on the reverse k-nearest neighborhood and the k-influence space[J]. Computer Applications and Software, 2022, 39(2): 287-293, 349.

GAO G S, CHEN X. An improved particle filter track-before-detect algorithm[J]. Application of Electronic Technique, 2020, 46(4): 61-65.

DAN Z Y, LIU F L, GONG D f. Smoothing filter detection algorithm based on middle and tail information of differential histogram[J]. Computer Science, 2021, 48(11): 234-241.

ZHANG H, ZHAO Z R, MEI H, et al. Land and water leveling data fusion processing method based on information spread estimation[J]. Journal of Gansu Sciences, 2022, 34(2): 11-14, 22.

WU Y Z, LI H R, YAO T, et al. A survey of multimodal information processing frontiers: Application, fusion and pre-training[J]. Journal of Chinese Information Processing, 2022, 36(5): 1-20.

GUO Y J, WEI Y D, JIN X H, et al. Fault feature extraction from time-frequency spectrum by using similarity measurement[J]. Journal of Vibration and Shock, 2020, 39(12): 70-77.

LI F, ZHANG X W, WANG B Q, et al. Research on similarity evaluation for target simulation model based on feature analysis[J]. Air&Space Defense, 2020, 3(4): 134-140.

YAO L Z, SONG F Y. Retrieval Method of document information resources in intelligent library based on rough fuzzy set[J]. Journal of Xinxiang University, 2021, 38(12): 48-51.

HU Z T, YANG S B, HU Y M, et al. Distributed fusion target tracking based on variational bayes[J]. Acta Electronica Sinica, 2022, 50(5): 1058-1065.