Soil microorganisms and mineral ions play a crucial role in the material cycle and energy flow. Different types of sandy, loamy and gravelly soils were selected as experimental sample plots, and the mineral element and microbial diversity of the soils were analyzed by using the curve method with spiked recovery analysis measurement and Illumina high-throughput sequencing technology. Then, principal component analysis and Pearson correlation analysis were applied to extract the factors affecting phosphorus and sulfur cycling by soil mineral ions and microorganisms, and the results showed that the mineral ions in the three different types of soils were mainly Na+, K+, Mg+, and Ca+. The top ten dominant bacterial phyla in relative abundance in different types of soils were Ascomycetes, Actinobacteria, and so on. The eigenvalues of the first four principal components in the principal component analysis of phosphorus-sulfur cycle influencing factors were greater than 1. Therefore, four principal components were selected: soil water content, soil Mg+ content, soil actinomycetes content, and soil Ca+ content.

Soil microorganisms are the main drivers in maintaining soil health. This paper focuses on the process of soil mineral ions and microorganisms involved in regulating the phosphorus-sulfur cycle, and systematically investigates the repair and improvement mechanism of soil microorganisms. Relying on an experimental area of a typical grassland in Inner Mongolia, we set up experiments with different nitrogen addition treatments, and combined with one-way analysis of variance (ANOVA) to investigate the distribution of soil phosphorus and sulfur fractions under various scenarios. Then, structural equation modeling was applied to explore the dynamic role between microbial action and phosphorus-sulfur cycle under N addition. Under different nitrogen addition scenarios, Ca10-P accounted for the largest proportion of inorganic phosphorus fractions, which were all greater than 40%. The percentage of inorganic sulfur in the soil was relatively small, less than 3% of total sulfur, and the response of inorganic and total sulfur to the gradient of nitrogen addition, nitrogen frequency, and different grassland management practices was not obvious. Fungal communities were important drivers of changes in functional genes for interleaf phosphorus and sulfur cycling at different N application levels, i.e., N fertilizer application altered the interleaf fungal communities by affecting soil physicochemical properties, which significantly regulated the interleaf bacterial communities, phosphorus and sulfur cycling functional gene abundance, and pathogenic fungal abundance.

Civil engineering disasters mostly occur in mountainous areas, and it is difficult to comprehensively monitor them using traditional technology, while this drawback can be avoided by utilizing UAV inclined photogrammetry technology. In this paper, with the support of the relevant experimental equipment, we obtain the images of civil engineering disasters with the help of this technology, and in order to avoid the influence of the interference factors in the images on the research results, we propose to use the K-means algorithm to pre-process the images. After completing the image processing, the improved YOLOV4 target detection algorithm is used to complete the design of the intelligent detection model of civil engineering disasters, and the processed images are input into the model for iterative training, so as to realize the intelligent management and early warning of civil engineering disasters. A region in Yunnan Province is taken as an example to explore and analyze the example. As of 2022, it is found that 180 landslides actually appeared in the region, while the model detected 172 landslides, resulting in the model’s civil engineering disaster detection accuracy of 95.56%, which is within the permissible range, proving that the model has a good application efficiency, and can provide certain help and innovative guidance for the relevant units of civil engineering disaster management.

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.