In recent years, the deepening of reform and opening up, the deepening of the socialization of college management, the trend of students’ thinking is more and more diversified leading to the frequent occurrence of college students’ behavior. This paper is based on Spark’s parallel H-mine cluster computing to mine the behavioral characteristics data of students in frequent item sets. Using the K-Means clustering algorithm optimized by information entropy and density, the clustering and classification process is carried out according to the central value of the obtained behavioral features. Construct the class model of student behavioral features, realize student behavior prediction by K-nearest neighbor algorithm, and build the early warning model of student behavior prediction based on Spark cluster. The results of clustering analysis show that the average number of times a class of students, the second class of students, and the third class of students eat at breakfast is 120.07, 107.66, and 118.25, respectively, and the first class of students has the most number of times of breakfast meals, which shows that this class of students has better eating habits. The number of students studying on March 24, 2023 is predicted by the model based on the K nearest neighbor algorithm, and the trajectory of the real value and the predicted value The number of students with relative error less than 0.2 accounted for 86.42%, indicating that the model is good at predicting the number of students as a whole.

AI technology in the development and application of traditional texture recovery and reproduction, deep learning models for traditional texture information and color information consistency migration is still deficient, this paper by using the visual Transformer network advantage and visual Transformer network Transformer encoder structure optimization. That is to say, in the Transformer encoder, the multi-head self-attention module and feed-forward network module are called to process the input data and extract the image features, and then join the edge preservation smoothing technology to remove the strong edge information, preserve some weak edges and local colors, and generate the image texture information with the input texture. The color interpolation method is used to achieve the consistency of texture color texture and image texture migration. The result images of Dong brocade texture style migration show that the image texture migration model based on visual transformer is more capable of generating images with the best style loss value and the best content loss value, and is able to obtain more than 70% of user preference.

For a long time, the cultivation and assessment of the practical application ability of piano in music education has been an important issue that people are constantly concerned about and trying to solve. The research uses the evaluation method based on fuzzy neural network to conduct the study, first of all, from the basic skills, performance skills as well as creative skills in three aspects of the construction of the students’ piano skills level index system, through the objective weight entropy weighting method to determine the weight of the index system on the students’ piano skills were assessed and analyzed, and got the indexes of the importance of the order of the subjective weighting order of the creation of skills (C, 0.471) > performance skills (B, 0.384) > basic skills (A, 0.145). 0.384) > basic skills (A, 0.145). After the selection of sample data, standardization of sample data and simulation training of the network model, the experimental results show that the application of the fuzzy neural network model for the evaluation of piano skill level is effective and feasible. The temporal accuracy and cognitive accuracy of piano playing were fused to quantitatively assess the brain function. The experimental results show that the brain function scores obtained with this method can effectively indicate that the students’ brain function increases with the increase of practice time and decreases with the increase of difficulty.

Artificial Intelligence AI composition is one of the hot topics that have been debated in recent years. In this paper, we first extract monophonic and chordal features from MIDI digital music files. Then the WaveNet intelligent music generation model is used as a carrier to optimize its multilayer convolutional network structure. The audio files are fed into the optimized WaveNet model, and the final training parameters are obtained after several rounds of iterative training. After the model completes the training, music sequences are automatically generated. The results show that the optimized WaveNet model for training leads to a significantly higher accuracy rate in the validation set than before optimization. Compared to other models, the method in this paper generates music using a larger variety of notes, improving the quality of the music theory and chord aspects. Compared with the composite scores of human compositions, the percentage of WaveNet model compositions with scores of 4 and above is about 20.3%, and the percentage of scores of 3 and above is 30.5%. Therefore, the overall level of the compositions generated by the model in this paper is good.

In the era of digital media, with the help of media empowerment, Chinese medicine culture dissemination completes the innovation from the two dimensions of disseminators and media channels, which brings new opportunities to Chinese medicine culture dissemination. Aiming at the problem of large time overhead of traditional greedy algorithm in the optimization of nodes of TCM culture dissemination network, NPG algorithm is used to optimize the influence of starting nodes, computational efficiency and selection strategy. On the basis of optimization, the propagation probability is calculated to determine that time, content and social relationship can be used as the basis for judging the propagation path, and the path coefficients are analyzed with the help of structural equations. The path coefficient of social relationship→time→Chinese medicine culture dissemination is 0.173, i.e., under the role of time, there is a significant direct effect between social relationship and Chinese medicine culture dissemination, and time plays the role of mediating effect in the reconstruction of dissemination path. The research in this paper promotes the sustainable development of Chinese medicine culture through the improvement of Chinese medicine culture communication network.

In today’s society, a single intelligent body does not meet the needs of complex tasks, and coordinated control of multiple intelligences becomes an important solution. In this regard, this paper carries out the research on the coordinated control strategy of multiple intelligences supported by deep reinforcement learning technology. Aiming at the problems of uneven task distribution and unsatisfactory decision consistency arising from the collaborative decision making of multiple intelligences under the software system architecture, a hierarchical multi-intelligence collaborative decision-making algorithm based on the AC framework is proposed to realize the information exchange and decision-making collaboration among intelligences, so as to improve the efficiency of coordinated control. However, with the increase of the number of multi-intelligents, the algorithm will have the problem of upper and lower level non-smoothness, in order to solve this problem, a multi-intelligents collaborative algorithm based on role parameter sharing is designed. Finally, the research scheme of this paper is evaluated and analyzed from multiple dimensions. When the number of intelligences increases by 5, the reward value of this paper’s algorithm does not show a decreasing trend, which indicates that this paper’s algorithm is able to handle the control coordination problem in the case of a small number of intelligences. When the number of intelligences increases by 15, the original method shows a decreasing trend, while in the multi-intelligence body collaboration algorithm based on the sharing of role parameters, the performance is very bright, which ensures the coordinated control effect of multi-intelligence bodies under the software system architecture.

The Chineseization of Marxism is one of the important topics of concern to Chinese social sciences. The study summarizes the main manifestations of the cultural identity of Marxist Chineseization, and estimates the potential growth rate of the Chinese economy using the extended Kalman filter algorithm from the dimension of material culture construction. Then based on CiteSpace, it conducts bibliometric measurements to explore the relationship between the Chineseization of Marxism and traditional Chinese culture. The measurement results of the model can better reflect the growth trend of the Chinese economy, and the economy will experience a period of medium-speed growth in the future, which should be seized to deepen the economic restructuring and promote the cultural identity of Marxist Chineseization by safeguarding the construction of material culture. The research literature on both the Chineseization of Marxism and traditional Chinese culture shows a general upward trend, especially from 2012-2021, with an increase of 3.06 times. The Chineseization of Marxism and Chinese culture have a deep-level fit, and the essence of Marxist ideology should be connected with the essence of Chinese traditional culture, so as to promote cultural identity and enhance cultural self-confidence in the process of the Chineseization of Marxism.

In order to improve the automation and intelligence level of underground fluid sampling, this paper proposes a kind of underground fluid automatic sampling device, and carries out the structural design of the sampling device, the control system design and the field experiment test. According to the action process and movement characteristics of the underground fluid stratified sampling device, the control system needs to use multiple electromagnetic control valves to control the switching of the oil circuit of different actuators respectively. In order to improve the control state and response speed of the stratified sampling device system, a fuzzy identification algorithm is chosen to identify the control model, and the MIMO robust generalized predictive controller is used as the robust adaptive controller of the system to realize the low-flow and low-disturbance acquisition of underground fluids at the same monitoring point and at different depths. In the field sampling, the average values of DO at sampling depths of 1m, 2m, 3m, 4m, and 5m for manual sampling, vertical sampling, and fuzzy adaptive device sampling under the 1-2 sampling plumb line were 7.98mg/L, 7.86mg/L, 8.25mg/L, 7.83mg/L, and 7.77mg/L, respectively. The deviation of dissolved oxygen content at the same sampling point in the three ways is small and the trend of change is consistent at different depths. It shows that the fuzzy adaptive stratified sampling device system designed in this paper can be applied to the sampling of subsurface fluids with dissolved oxygen as the detection target.

In this paper, finite element simulation of heat transfer process is carried out using Cu composites reinforced with TiB2 of different particle sizes. Based on the FEA data, the BP neural network algorithm is integrated and optimized by the MEA algorithm to establish the FEA-MEA-BP performance prediction model. The results of thermal conductivity analysis show that the correction factor of the simulated thermal conductivity value of TiB2/Cu composites can be calculated using the finite element method as 2.3. Compared with the actual value measured by the LINSEISLFA1600 laser thermal conductivity meter, the fluctuation of the simulated thermal conductivity results from the experimental results is no more than 10% between 50~200°C, and the simulation performance has a high degree of accuracy. Taking 304L stainless steel as a sample, the RMSE, MAE and R² are improved to different degrees compared with other models, so the performance of the FEA-MEA-BP model is excellent in terms of the accuracy of prediction.

The article is based on the need for music education innovation in colleges and universities to optimize the traditional piano skill training through Monte Carlo algorithm. Taking the finger as the research entry point, based on the physiological structure of the hand, the reduced-density Monte Carlo method is used to carry out the mechanical design of the finger trainer and plan the finger training movement mode. Through kinematic simulation experiments to understand the feasibility of the piano finger training device in this paper. Analyze the error sensitivity of position and posture on the finger training device. Finally, the teaching experiment method is utilized to explore the training effectiveness of the Monte Carlo-based piano finger training device in this paper. This paper has good usability. When the position error of the mechanism varies in the range of -40mm~40mm, the position error gradually decreases in the X-axis and Z-axis, and the position error in the direction of Y-axis remains stable. The attitude error of the mechanism gradually increases with the increase of the X-axis rotation angle. The output accuracy gradually increases during the rotation from -5° to 5° around the Y-axis. The angular attitude around the Z-axis has no significant effect on the output accuracy. The two groups did not have significant differences in the four dimensions of piano playing skills before the experiment. After the teaching experiment, the experimental group was much better than the control group, and the posttest results of the two groups produced significant differences, and the pre-test and post-test results of the experimental group possessed very significant differences. The Monte Carlo optimization-based piano finger training device has a significant effect on the improvement of students’ piano skills.