Based on the research of digital economy and digital transformation of manufacturing industry, the article constructs the evaluation index system of digital economy development level and digital transformation of manufacturing industry in Yangtze River Delta respectively. The entropy weight TOPSIS method is used to measure and analyze the level of digital economy development and the level of digital transformation of manufacturing industry in the Yangtze River Delta respectively. The coupling coordination degree of digital economy and manufacturing digital transformation in the Yangtze River Delta in general and in each province and city is calculated and analyzed. On the basis of the relevant conclusions, recommendations for the digital transformation of the manufacturing industry and the elimination of the “digital divide” in the Yangtze River Delta are proposed. The overall trend of digital economy in the Yangtze River Delta (YRD) is on the rise, but there is a significant “digital divide”. The increase in the level of digital economy development in the Yangtze River Delta over the past 10 years is 10.76%. The average value of digital economy development water in Shanghai, Jiangsu, Zhejiang and Anhui is 0.358, 0.549, 0.491, 0.185 respectively, and the development of digital economy in Anhui is insufficient. Shanghai’s manufacturing digitalization water average is 0.750, the highest level in the Yangtze River Delta. Zhejiang and Anhui are slightly behind in manufacturing digital transformation. The coupling coordination degree of digital economy and manufacturing digital transformation in the Yangtze River Delta grew from 0.592 to 0.879, and the type of coupling coordination development shifted from barely coordinated to well coordinated. Shanghai and Jiangsu have reached the good coordination level. Zhejiang is at the intermediate coordination level. And Anhui is at the barely coordinated stage, which is the lowest level in the Yangtze River Delta.

As a folk traditional handicraft with a long history in Guangxi, mango weaving craft is facing the dilemma of shrinkage and decline. Starting from the application of AI technology in the protection and innovation of mang weaving technology, the article analyzes the text of collected mang weaving product reviews with the help of the perceived value theory and LDA topic model, and mines the consumer preference of mang weaving in order to better improve the production and manufacturing of mang weaving. Meanwhile, a lightweight generative adversarial network based on non-local attention mechanism is proposed to realize the construction of text-to-multi-objective image generation model, which is used in the innovative design of mango knitting. Consumers are more satisfied with the overall experience of mango knitting, and positive emotions account for 82.6%. The review texts were categorized into five themes with probability values of 0.181 to 0.227, and improvements should be made to the mango knitting product in terms of emotion, quality and price. The text-generated image model is able to output the mango knitting design images well, and the average values of IS and FID are 21.85% and 16.46% better than the comparison model, and the quality of the output images is better than the other models in different test sets. With the help of AI technology to improve the process of mango weaving, to enhance the design creativity of product quality, and to broaden the channels for the inheritance and development of mango weaving skills.

Artificial Intelligence has been applied in many aspects of life, however, AI algorithms have been less used in the field of music. In this paper, a multi-track based pop music generation model MuseGAN is proposed, due to its poor contextualization and excessive tempo jumps in generating pop music samples. In this paper, a new multi-track pop music generation model-Recurrent Feature Generation Adversarial Network RFGAN is proposed. the model addresses the temporal relevance of the music structure and the repetitive nature of the musical section, and proposes a temporal model that enhances the contextual relevance of the music samples in terms of the time series, and improves the generative model according to this temporal model by converting the unidirectional structure in the original model to a recurrent structure, adding the feature extractor to the previous level of training information, which is combined with arbitrary noise and passed to the next training. An average pooling layer is added at the end of the generative model as a solution to the situation where the model generates too much noise for pop music samples. The improved model is superior to the pre-improvement model in terms of stability, convergence speed, and overfitting in pop music generation. In the audience scoring experiment, 60% of the top 5 pop music scores were generated using the RFGAN model proposed in this paper, indicating that the pop music generated using the RFGAN model has reached a high level comparable to the level of artificial pop music composition.

Groundwater seepage has a greater impact on the stability of foundation engineering, and it is also an important factor that restricts the development of geotechnical engineering projects and the quality of engineering surveys. In this paper, tunnel engineering is selected as the foundation engineering project under study to investigate the specific influence of groundwater seepage in the process of tunnel excavation. The flow-solid coupling model is constructed, and the safety coefficients of the tunnel project in different situations are calculated based on the strength discount method and the ultimate strain method. Numerical analysis software is used to establish the calculation model of the influence of groundwater seepage on the stability of tunnel excavation. The displacement of surrounding rock around the hole is selected as the evaluation index of tunnel stability, and the effect of groundwater seepage on each index is calculated by the analysis software. The study shows that groundwater seepage will make the rock body around the palm face after tunnel excavation change significantly from the pre-excavation bending. The seepage increases the displacement of the surrounding rock, and the coefficient of increase of vertical displacement is larger than the coefficient of increase of horizontal displacement. At the same time, the flow-solid coupling effect of groundwater seepage increases the surrounding rock stress, and the increase coefficients of each key point of the tunnel excavation are between 1.11-1.50, resulting in significant deformation of the bottom of the arch, the top of the arch, and the arch girdle. In addition, the groundwater seepage makes the tunnel safety coefficient decrease from 9.03 to 5.18, which significantly causes the decrease of tunnel stability.

In the current business environment, artificial intelligence is becoming a key force driving performance management and organizational change. In this paper, finance, customer, internal process, learning and growth are selected as the indicators of performance evaluation of Enterprise A through the balanced scorecard model, and the fuzzy algorithm is used to provide comprehensive scores and grades for the above indicators. In addition, this paper sets up an organizational structure change evaluation model and analyzes the effect of the system on the organizational change of Enterprise A through the measurement of key indicators. The recognition rating of the questionnaire set up in this paper by the employees of Enterprise A is 4.15, and the recognition degree of the Intelligent Performance Management System is “basically recognized”. The intelligent performance management system improves the design and execution of internal processes in Enterprise A, and promotes the organizational change of Enterprise A in terms of total resource utilization. In conclusion, this study provides reliable technical support for enterprise performance management and organizational change.

Cultural heritage is an important symbol of the historical and cultural achievements of a country and a nation, and it is of great significance for the study of the evolution of human civilization, and the protection of national languages and scripts has also become the primary task of national work. The study is based on virtual simulation technology to design a virtual pavilion of Chinese language and writing, using image and text feature extraction algorithms and related technologies for image feature fusion and three-dimensional modeling of Chinese language and writing. The extraction effect of Chinese language and characters is verified by the matching of feature points, and the protection and inheritance effect of the virtual exhibition hall is reflected by the user experience score. The results show that the image and text feature extraction algorithm proposed in this paper has a good extraction effect, and there is no misrecognition. The order of users’ interest in each influential factor of the virtual pavilion of Chinese language and writing is text image>visual image>pavilion experience>scene art>language culture>image color>font charm>cultural art. Through the analysis of the feedback data of the eight subjects’ user experience, it can be seen that the average value of the total score of the user experience is more than 60 points, and the experience effect is good, which achieves the role of the inheritance and protection of Chinese language and writing culture.

In recent years, due to the adjustment of economic structure, the people’s living standard and the increase of leisure time, the sports industry has become a new economic growth point. This paper studies and analyzes the characteristics of the industry background and business background of the sports industry, explores the factors and internal driving force affecting the design of its business model, and fully analyzes the mechanism, functional role, and logical relationship of the elements for constructing the business model of the sports industry, and then explores the characteristics of the business style of the sports industry. From the perspective of knowledge state, using the reinforcement learning mechanism, the evolution process of the sports industry business model from the first stage to the fourth stage is described. Taking Company H as a research case, the process and economic effect of the transformation and upgrading of its business model through the reinforcement learning mechanism is analyzed and it is found that as of 2023 the company’s operating income has increased by 2.4 times through transformation and upgrading, and its net profit has increased by 125.57 percentage points compared to 2016. It further understands the role that the enhanced learning mechanism brings to the development of the sports industry, and expects to be able to provide a reference for the sports industry to carry out business model transformation in the future.

Blockchain technology has the characteristics of data anti-tampering and anti-forgery, which can provide solution ideas for the secure storage and transmission of data in distributed networks. The study applies blockchain technology to data auditing, constructs an aggregated signature based on conditional identity anonymization to protect user privacy, simplifies the auditing computation by using homomorphic hash function, and deploys three kinds of smart contracts on the blockchain to design a blockchain-based data integrity auditing scheme. For the privacy protection problem, a blockchain privacy protection model based on differential privacy is constructed by integrating the differential privacy policy into the blockchain smart contract layer. The experimental results show that the data integrity auditing scheme has superior blockchain storage cost and time overhead, and the average time overhead under different dynamic operations is below 30ms. The privacy protection model also exhibits high efficiency, with encryption and decryption times of 0.075s and 0.063s, respectively, under the largest data file, and a significant speed advantage in all phases of operation. The proposed scheme in this paper meets the needs of data integrity and privacy protection, and can provide efficient services for users.

Colleges and universities are an important part of higher education, providing a large number of talents for social development. The study optimizes the way of student management in colleges and universities based on artificial intelligence technology. Firstly, the K-means algorithm in cluster analysis is used to classify students’ campus behavioral characteristics. Then use Apriori algorithm to correlate students’ behavioral characteristics with academic performance. Finally, colleges and universities can take differentiated management measures for different categories of students. The clustering analysis of 12,885 students’ consumption behavior, work and rest behavior, and study behavior in college Z, followed by the correlation analysis between the clustering results and academic performance, and a total of 10 correlation rules were found. Colleges and universities can formulate management rules based on the analysis results to improve management efficiency. In addition, the student management work of colleges and universities can be optimized and upgraded in several directions, including the awareness of student management work in colleges and universities, the information platform, the archive management work, the student management team, and the information security work.

Under the background of the current information age, the electronic and intelligent transformation of the bidding industry has become an inevitable trend, and the e-bidding model stands out and greatly improves people’s understanding of bidding. Aiming at the traditional e-bidding system, in order to solve the problem of the lack of the traditional e-bidding system that provides the bidding body with referable opinions, this study firstly constructs the e-bidding risk assessment indexes and realizes the optimization of the evaluation module of the system. Then the recommendation algorithm based on deep learning implements the optimization design of the e-header bidding system. This study constructs an optimized recommendation model by fusing knowledge graphs on the basis of deep learning. Then the e-tendering optimization system is designed according to the actual needs of e-tendering, combined with the recommendation model of this paper. The accuracy index ACC of this paper’s recommendation model is improved by about 3% on average compared with other best-performing recommendation models on each dataset, which verifies the excellent performance of this paper’s recommendation algorithm. This study constructs an optimized e-tendering system and proposes suggestions for the development and operation strategy of corporate e-tendering, contributing to the development of e-tendering transactions and the participation of social capital.