Analysis and Evaluation Research on the Construction of the Music Art Management Curriculum System Based on the Deep Learning Model

Hu, Shengquan; Yang, Lei

doi:10.1155/2022/5873218

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…is has improved a certain reference value and reference for the school's music teaching and management. Hu and Yang [16] have found that school music teaching has the problems of few courses and lack of music management. In order to improve students' interest in music learning, it uses deep learning technology to study its role in music teaching management.…”

Section: Related Workmentioning

confidence: 99%

Design of the Music Intelligent Management System Based on a Deep CNN

Shang

Shao

2022

Security and Communication Networks

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Music is a common art form in people’s life, and it is closely related to people’s living conditions. Since ancient times, music has been closely related to people’s lives. The music intelligent management system is convenient and user-friendly, and it can meet the demand for music. However, it has major flaws. Collaborative filtering algorithm can achieve the recommendation performance of music intelligent management system, which can recommend the same type of music to users with related preferences. Deep learning technology has developed relatively maturely, and it has been successfully applied in people’s life and production. Deep convolutional neural network (CNN) techniques can extract deeper features than simple CNN techniques. Although there is a weak nonlinear relationship between people’s behavioral characteristics and living habits and the music intelligent management system, the advantage of deep CNN technology is to deal with the nonlinear relationship between large amounts of data. This study uses deep CNN technology to extract the relationship between people’s living habits, living environment, and behavior characteristics and the music intelligent management system. The deep CNN technology helps the music intelligent management system to further realize the active recommendation function of the music intelligent management system. The research results also show that the deep CNN technology has good feasibility and high accuracy in the music intelligent management system. It can well map the relationship between people’s behavioral characteristics and living habits and the music intelligent management system. The deep CNN technology can also realize the active recommendation function of the music intelligent management system. For the prediction of the music intelligent management system, the largest prediction error is only 2.17%. This part of the error is for the prediction of song genres. The prediction errors for the other two features are both within 2%.

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Section: Related Workmentioning

confidence: 99%

Design of the Music Intelligent Management System Based on a Deep CNN

Shang

Shao

2022

Security and Communication Networks

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“…Big data theory is also widely used in image recognition and video feature recognition, which also brings great convenience to people's life and production. In life, the characteristics of research objects in production or life are mainly divided into three types: spatial characteristics, temporal characteristics, and environmental characteristics [ 19 , 20 ]. It also has many corresponding algorithms.…”

Section: Introductionmentioning

confidence: 99%

Analysis the Innovation Path on Psychological Ideological with Political Teaching in Universities by Big Data in New Era

Luo

2022

Computational and Mathematical Methods in Medicine

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The increasing pressure of life and the rapid development of the economy have caused huge mental health situation problems for people. Mental health situation has an important relationship with one’s own life values. The cognition Ideological with political is to improve people’s optimistic attitude and values. The students of college will face enormous pressure from their studies, which can easily causes the mental health situation problems. However, the psychological and ideological education in universities still adopts the traditional teaching method, which reduces students’ learning hobbies and learning efficiency. This also reduces students’ understanding of the content of psychological ideological with political teaching. Big data theory can process complex research object data and relationships. It can help researchers discover characteristic factors related to psychological ideological with political teaching. This study uses the hole convolution in big data theory and GRU technology to analyze the three factors of student behavior characteristics, mental health situation content characteristics, and the cognition Ideological with political content characteristics in college psychological the cognition Ideological with political. The research results show that the atrous convolution and GRU methods can more accurately predict the three characteristics of psychological ideological with political teaching in universities. This is helpful for educators to discover more appropriate psychological and ideological with political teaching methods.

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Retracted: Analysis and Evaluation Research on the Construction of the Music Art Management Curriculum System Based on the Deep Learning Model

Sensors

2024

Journal of Sensors

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Analysis and Evaluation Research on the Construction of the Music Art Management Curriculum System Based on the Deep Learning Model

Cited by 3 publications

References 7 publications

Design of the Music Intelligent Management System Based on a Deep CNN

Design of the Music Intelligent Management System Based on a Deep CNN

Analysis the Innovation Path on Psychological Ideological with Political Teaching in Universities by Big Data in New Era

Retracted: Analysis and Evaluation Research on the Construction of the Music Art Management Curriculum System Based on the Deep Learning Model

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