Abstract With the progress of the times, the rapid development of socio-economy, and changes in human ideology, traditional martial arts have become more prominent based on ancient philosophy and ethics. However, due to modern science and technology advancements, this art is facing unprecedented challenges. This paper aims to explore the development process of traditional Chinese martial arts and achieve this goal using deep learning methods. To... With the progress of the times, the rapid development of socio-economy, and changes in human ideology, traditional martial arts have become more prominent based on ancient philosophy and ethics. However, due to modern science and technology advancements, this art is facing unprecedented challenges. This paper aims to explore the development process of traditional Chinese martial arts and achieve this goal using deep learning methods. To achieve this, we have taken a series of measures, including searching, integrating, summarizing, and classifying, better to grasp the skills of various martial arts masters and accurately measure their inheritance status. Through this study, we conclude that by using deep learning, we can accurately identify the characteristics of various traditional Chinese martial arts and thus better evaluate their value. This achievement brings a new perspective and approach to protecting and disseminating traditional Chinese martial arts. Read More Read Less

Abstract In recent years, the advancement of artificial intelligence technology has become a significant driving force for digital music creation, attracting widespread attention. Compared to the high cost and uncertain timing of manual creation, the extreme cost-effectiveness of AI music can effectively help developers reduce costs and increase efficiency. Therefore, the automation of digital music creation has broad applications and significance. The recursive neural network is... In recent years, the advancement of artificial intelligence technology has become a significant driving force for digital music creation, attracting widespread attention. Compared to the high cost and uncertain timing of manual creation, the extreme cost-effectiveness of AI music can effectively help developers reduce costs and increase efficiency. Therefore, the automation of digital music creation has broad applications and significance. The recursive neural network is an artificial intelligence algorithm based on neural networks, mainly with a loop structure, that can adaptively process sequence data. In a digital music creation system, recursive neural networks can predict and generate note sequences, thereby achieving automated music creation. Various factors, such as the quality of training data, the settings of model parameters, and the stylistic characteristics of the music influence the predictive results of recursive neural networks. Therefore, meticulous parameter tuning and optimization need to be conducted for different application scenarios. Read More Read Less

Abstract With the rapid development of artificial intelligence, it has been widely applied in various industries in recent years. It is a very important topic for courses, so we must continuously improve our course content. This article will study how to use artificial intelligence to establish an effective course management system to effectively disseminate our courses. Through a systematic exploration of deep learning mathematical models, we... With the rapid development of artificial intelligence, it has been widely applied in various industries in recent years. It is a very important topic for courses, so we must continuously improve our course content. This article will study how to use artificial intelligence to establish an effective course management system to effectively disseminate our courses. Through a systematic exploration of deep learning mathematical models, we find that they can significantly change the teaching of courses. Through practical simulations, it can better help schools organize classroom content and inspire learners’ enthusiasm for learning. Read More Read Less

Abstract This paper is based on the dance movement recognition model of 3D Convolutional Neural Networks (CNNs) and aims to explore the feasibility of applying intelligent technology to dance teaching. By investigating the current status of intelligent technology in dance teaching both domestically and internationally, the advantages of 3D CNNs in dance movement recognition are analyzed. In response to the needfor dance movement recognition, a model... This paper is based on the dance movement recognition model of 3D Convolutional Neural Networks (CNNs) and aims to explore the feasibility of applying intelligent technology to dance teaching. By investigating the current status of intelligent technology in dance teaching both domestically and internationally, the advantages of 3D CNNs in dance movement recognition are analyzed. In response to the needfor dance movement recognition, a model based on 3D CNNs is designed and validated using the MSRAction3D dataset. The experimental results show that the model achieves high recognition accuracy in 20 dance movement categories, proving its potential application in dance teaching. This model can provide dance teachers with accurate movement recognition and personalized guidance, improving teaching effectiveness. Read More Read Less

Abstract Constructing an evaluation system for the practical effects of political education courses is beneficial for assessing existing educational work and serves as a “compass” for improving corresponding work. Therefore, this study explores the practical effects of college educational courses based on a computer artificial neural network model. The article first establishes a “three-dimensional index system” for evaluating educational courses in colleges based on the characteristics... Constructing an evaluation system for the practical effects of political education courses is beneficial for assessing existing educational work and serves as a “compass” for improving corresponding work. Therefore, this study explores the practical effects of college educational courses based on a computer artificial neural network model. The article first establishes a “three-dimensional index system” for evaluating educational courses in colleges based on the characteristics of artificial neural networks. Then, it introduces the computer artificial backpropagation (BP) neural network evaluation method and obtains strong empirical support through simulation experiments. Finally, a feasible “college artificial neural network evaluation model” is constructed. The simulation experiments show that the model’s actual output is very close to the expected results, and satisfactory results are achieved when testing the model with sample data. Using the BP neural network model effectively eliminates traditional evaluation methods, significantly increasing the implementation efficiency of the evaluation. The improvement of the BP neural network enhances its flexibility and performs excellently in training. The model’s expected performance in simulation experiments is highly consistent with actual performance, and it also achieves satisfactory results when measuring its performance using test data. In college education, it is a widely used evaluation method. Read More Read Less