The application of artificial intelligence on the field of art can be used to assist the creation of musicians and provide new creative ideas for musicians. In this paper, firstly, an ARIMA model is established for the prediction problem of opera style, which is used to predict the trend of the development of opera style sequence, and the best model is selected according to the minimum information criterion and Bayesian criterion. Then an automatic music melody generation method based on the generative adversarial network framework is proposed, which applies the trained natural language generation model to music generation to textualize the music melody and reduce the model running time. In addition to this a barization music melody generation method is also used, which divides a large music melody into melodic segments and generates them segment by segment, reducing the difficulty of the model in generating the music melody. Finally, the Fourier transform method is used to extract the features of the music melody and complete the visualization of the music melody. The model ARIMA(2,1,1)(2,1,0)12 that best fits with the time-series prediction of the development of opera styles was identified through empirical analysis. The PB value of Leak-GAN_2 model in this paper is improved by 41.38% compared with MusicGAN. It shows that both the opera style prediction model and the music melody multimodal generation model constructed in this paper have better effect and certain advancement.

In order to improve the attendance rate of students and optimize the quality of teaching, this paper proposes a method of predicting the attendance rate of students in colleges and universities based on multivariate regression analysis. Firstly, we obtain the factors affecting students’ attendance rate through sample survey and conduct correlation analysis, and then summarize and refine the three dimensions of students, teachers and schools. The above dimensions are used as independent variables to construct regression equations, and the regression equations are used to predict the attendance rate of students, so that teaching managers can optimize the management. The analysis found that the factors such as the college to which the truant students belonged, the reason for truancy, and the grade level showed diversity and complexity. Overall male students have more truancy rates than female students, and lecturers with higher titles have lower truancy rates. Regression modeling and prediction of truancy rate found that the prediction results are closer to the real results. Therefore, the method of this paper can be combined to optimize and adjust the attendance rate from the aspects of regulations, work allocation, teaching management and ideological education.

Personalized learning, in which learners set their own pace and select their own resources according to their own learning needs and characteristics, is the trend of Chinese education and teaching. In this paper, we design a personalized teaching path recommendation model for Chinese education based on reinforcement learning. The knowledge tracking prediction model LTKT is designed to integrate multiple knowledge points as information dimensions for model learning in the data preprocessing stage. The sparse self-attention mechanism is introduced into the encoder and decoder structure and embedded with location coding containing absolute and relative distances to enhance the model’s perception of location information. Finally, the RL4ALPR algorithm is designed to model the changing knowledge level, the candidate learning item filtering algorithm is used to narrow down the scope of the recommended learning items, the reinforcement learning algorithm assumes the role of a recommender, and the degree of change in the knowledge level of the learner is regarded as a reward for the improvement of the reinforcement learning recommendation strategy. Simulation experiments are conducted on datasets such as ASSISTments and compared with baseline models such as KNN, GRU4Rec, Random, etc. The model in this paper has an F1 value and an AUC of 0.635 and 0.956 respectively in the evaluation of learning effect, which are the highest among the models. The study makes a useful exploration for the informatization of Chinese education and teaching.

The mechanism study of steel pipe welding in Dianzhong water diversion project is very complicated, and there are many process parameters affecting the temperature distribution of high-frequency heating of welded steel pipe, and the degree of influence and the influence law are not the same. In this paper, Abaqus software is used to carry out the finite element analysis of the steel pipe welding process, and the displacement variational method (i.e., Ritz method) is introduced to derive the radial displacement of the steel pipe when it is subjected to the action of the centralized force, so as to realize the finite element simulation of the welding process of the steel pipe. At the same time, the optimization of the welding process parameters of the steel pipe is realized by combining the radial basis function neural network (RBF) and particle swarm algorithm (PSO). The simulation results show that the Von mise equivalent residual stress at the weld seam reaches the nominal yield strength of the material on both the internal and external surfaces of the steel pipe, while the axial residual stress has a very different distribution law on the internal and external walls of the steel pipe, which belongs to the tensile stress and weld residual compressive stress at the weld seams on the internal and external walls of the steel pipe, which are about 0.4 times the yield strength of the material and 0.7 times the yield strength of the material, respectively. The ring residual stress distribution law of the steel pipe is similar to the axial residual stress, but both reach the nominal yield strength of the material. Through parameter optimization, this paper determines that when the opening angle is 5°, the current frequency is 217.35 kHz, and the distance from the coil to the V-point is 252 mm, the corresponding optimization target values are all smaller, and the welding quality of the corresponding weld seam is better. The research in this paper provides a theoretical basis for further improving the welding quality of steel pipe in Dianzhong water diversion project.

The field of education is paying more and more attention to the fundamental task of education by establishing morality, and ideological and political education has become a major project in which all the teaching and learning links cooperate with each other and are accomplished in a concerted manner. This study explores the method of organic integration of ideological and political education and teaching and data visualization technology to enhance the effect of ideological and political teaching. Firstly, the method of portrait construction is introduced, combined with the student behavior dataset, and the student behavior data is preprocessed. Using the user portrait construction method as a hub, a gradient boosting decision tree model was used to predict the students’ Civics learning performance. The improved K-prototypes clustering algorithm was used to categorize student groups, which facilitated teachers to develop targeted learning strategies. Finally, group portraits and feature labels are extracted from the students to further help teachers accurately determine the types of student groups and carry out personalized teaching. The classroom teaching model in this paper classifies students into four categories with obvious behavioral characteristics, which increases teachers’ understanding of students, and the model not only improves students’ academic performance in Civics, but also significantly improves students’ level of course Civics and increases students’ classroom active response rate by 19.625%. The Civics education data visualization technology proposed in this paper reveals the rules of Civics education and improves teachers’ work efficiency.

This study aims to investigate the influence of university language education on students’ expressive ability, and uses a questionnaire to collect the relevant factors affecting the relationship between students’ expressive ability and university language education. The key principal factors were extracted from many variables by principal component analysis to simplify the data structure and retain the main information. Subsequently, a multiple linear regression model was constructed and the least squares method was applied to estimate the model parameters in order to quantitatively analyze the linear relationship between each principal component and students’ expressive ability. In this paper, four principal factors, namely, “language organization ability, communication ability, language use ability and intonation ability”, were identified under the principal component analysis technique, and their total variance explained reached 56.326%. It is found that the average score of students’ expression ability is in the middle normal level, but the extreme difference of score between different students is as high as 27, which shows that there is a big gap between students’ expression ability. The correlation coefficient between students’ expressive ability and university language education is 0.8947, and the correlation coefficients of the four sub-dimensions of the two sig values are less than 0.01, indicating that the stronger the university language education, the higher the level of students’ expressive ability. And the regression equation of students’ expression ability and university language education is obtained as Y=0.893X-15.874.

In this paper, time series analysis is used to monitor and predict the performance of athletes in sports training. A smooth time series model ARMA p q   , model is established, a fixed-order method based on autocorrelation function and partial correlation function is proposed, and the parameters of the model are estimated, and least squares prediction is used for model prediction. The monitoring test data of hemoglobin (HGB) in sports performance of Z athletes of a club were used as the research object, and the smooth time series test was conducted to determine the ARMA (1,1) model as the optimal time series fitting model, and the fitting effect was tested. In the application of blood oxygen saturation (BOS) index, ARMA (1,1) model can predict the trend of BOS of athlete Z with good application effect. Based on the prediction of athletes’ performance by ARMA (1,1) model, this paper further proposes the integrated neuromuscular training method (INT), and integrates it with physical training will to develop the INT physical education training strategy. In the application experiment of INT physical education training strategy, the test results of the experimental group of athletes applying the INT physical education training strategy in the six events of T-test sensitive running, agility ladder, vestibular step, blindfolded one-legged standing, 30-meter sprint running, and 60-meter sprint running presented P<0.05, and the athletes' performance was significantly better than that of the control group.

Teachers and students will form a variety of dependent behaviors and interactions centered on teaching activities in the teaching process, thus, the teaching process can be regarded as a typical game process. This paper invokes game theory, takes teacher-student behavioral interaction as the research object, constructs a game model of teacher-student behavior in the process of English teaching, and proposes a teaching optimization strategy for English flipped classroom. At the same time, numerical simulation of the teacher-student game model is carried out to explore the dynamic game equilibrium under the cooperative behavior of teachers and students. The simulation results show that in the teacher-student game network, the strategy choices of teachers and students change over time, and different benefit-loss parameter μ, additional gain parameter β₀, and cost-saving parameter ψ have a greater impact on the replication of the strategy choice behaviors of the game parties. In addition, the increase of the parameters of the gain PT obtained by the instructor’s conscientious instruction, the gain PS obtained by the student’s conscientious learning, and the loss KS of the punishment that the student receives for not learning conscientiously are conducive to the promotion of the instructor and the student’s strategy evolution towards cooperation (conscientious instruction, conscientious learning), while the increase of the instructional cost CT of the instructor’s conscientious instruction and the learning cost CT paid by the student’s conscientious learning are not conducive to the promotion of the two parties’ cooperation. And when the proportion of instructors and students initially choosing cooperation is larger, the likelihood of both parties evolving toward cooperation is greater. This paper provides theoretical support for the optimization of English teaching process.

College students’ physical fitness is an important part of national health, and analyzing physical fitness data in college physical education teaching helps to dig out the factors affecting students’ physical fitness and adjust the teaching plan in time. The article reviews some basic regression tools and selects variables such as BMI dietary habits for logistic regression analysis to analyze the factors affecting students’ physical fitness. The similarity, uncertainty and dissimilarity between students and their friends are calculated by Top-N recommendation set algorithm, and the physical education teaching program is dynamically adjusted with the new SFD recommendation algorithm. Finally, values were assigned to different movement banks and risk factors, and the experts’ agreement with the new adjusted program was examined. The intensity of physical activity had the greatest relationship with passing or failing physical fitness among all factors (regression coefficient = 0.927, p70%), reflecting the rationality and feasibility of this study.

In order to optimize the pattern design method in lacquerware decoration design, this paper first analyzes the discrete and continuous situation of the pattern in time and frequency by Fourier transform method, and explains the mapping principle of Fourier variation. After that, the original image is processed such as sharpening and smoothing under the Fourier transform algorithm, and the lacquer decorative pattern after automatic deformation is obtained through interaction on the basis of 2D affine transformation technology. Finally, the geometric deformation of the lacquer decoration design from 2D to 3D is simulated and verified. The results show that in this paper, the threshold value, brightness and contrast of the lacquer decorative design patterns can be obtained by the geodesic distance deformation algorithm under the Fourier transform in MATLAB software to get the geometric patterns of the lacquer decorative design with the main color of the appropriate filler blocks. The corresponding blue values of the four patterns are 418, 38, 104 and 256; the optimal values of green are 256, 100, 87 and 405; and the optimal values of red are 256, 57, 63 and 117. 3-D imaging simulation experiments show that the average absolute error, root mean square error and maximum absolute error of the depth of the geometric patterns of the 3-D imaging method and the geometric patterns proposed in this paper are all significantly reduced, and the depth of the geometric patterns in the 20- mm depth range are reduced significantly. and the advantages of this paper’s method are more obvious in the depth variation range of 20mm. It can be seen that the algorithm of this paper can improve the deformation effect of geometric patterns in lacquer decorative design.