Aiming at the difficulties faced by traditional industries, this paper formulates a smart blockchain solution for sustainable industrial digitalization. Through the theoretical analysis of blockchain technology integration into industry, it provides theoretical support for the application of intelligent blockchain technology in industrial digital transformation. Combining the above three algorithms and the actual situation of industrial digitalization development, an industrial digital transformation scheme integrating intelligent blockchain technology is designed, and a case study of the scheme is conducted. The delay mean value of this paper’s scheme is within the allowable range at the maximum throughput, indicating that the scheme can promote the sustainable development of industrial digitalization. In the actual application scenario, the CD-PBFT consensus algorithm performs more prominently, and in addition, it can be seen that the industrial blockchain solution, which can enhance the product recycling rate, well practices the concept of sustainable development.

The emergence of artificial intelligence has changed the traditional visual communication design mode to a great extent. This study aims to conduct an in-depth theoretical discussion and empirical analysis of the intersection of artificial intelligence and visual communication design, for the generative design application of AI technology in visual communication design, based on the AttnGAN algorithm, designing the adaptive word attention module and feature alignment module, constructing the ACMA-GAN text image generation model, and evaluating its visual communication design by combining quantitative and qualitative experiments to assess its The effect of ACMA-GAN on visual communication design is evaluated by combining quantitative and qualitative experiments. Combined with OLS algorithm, the empirical analysis of the effect of AI technology on visual communication design is carried out, and the ACMA-GAN model achieves excellent performance in the evaluation of assisted visual communication design, with the BLEU-3 and CIDEr scores higher than the next highest scores by 7.48% and 7.35%, and the average scores of each qualitative index are over 4.5, which indicates the feasibility and good utility of AI technology in assisting visual communication design. AI technology can positively act on visual communication design through image recognition and analysis, image generation and creation assistance, personalized design and workflow optimization.

This paper adopts research methods such as literature method and questionnaire survey method to take the cultural inheritance and development of Lanzhou Taiping Drum as the research object, and conducts in-depth discussion on the characteristics, social background and development of Lanzhou Taiping Drum. The research and analysis of the influence of the inheritance of Lanzhou Taiping Drum was also carried out by using principal component analysis and stepwise regression method in combination with the actual situation. It is found that many factors of Lanzhou Taiping Drum itself and government factors have significant influence on its inheritance. On the basis of the results of this study, we explore the ways and contents of the protection and inheritance of Lanzhou Tai Ping Drum, and put forward the digital inheritance of Lanzhou Tai Ping Drum and the path of cultural ecological reconstruction in terms of the influencing factors.

In today’s digital era, user interface (UI) design is crucial for enhancing user experience and strengthening user engagement. The study uses heatmap analysis, K-means clustering algorithm and random forest regression algorithm to comprehensively analyze the characteristics of user behavior in UI pages. The predicted results of user behavior in UI pages are visualized and analyzed through heatmaps. Cluster classes are divided according to user behavioral characteristics to generate user profiles with the same behavior. Combine Random Forest and Logistic regression algorithm to get the key indexes of UI optimization design and predict their impact on user behavior experience. The research results show that the MAE and SMAPE values of Random Forest regression algorithm on user behavior prediction are 133.55 and 8.18%, respectively, with an R² of 0.96, and the accuracy rate of behavior prediction is more than 80%, which shows a good performance of user behavior prediction. The clustering algorithm divides the user behavioral characteristics into 6 clusters based on their behavioral characteristics, including cluster class 1 (browsing and exploring class), which accounts for 11.5% of the number of investigators. The weight of the top 8 of the importance of UI optimization design obtained by the random forest regression analysis algorithm is 70.26%. And the user behavior experience can be improved by 5.377~9.925 times when each element is improved by one unit.

This topic obtains the data of featured vocabulary under the technical architecture of big data platform and saves it in the form of dataset. Standing on the perspective of the principle of translation of featured words in foreign propaganda, the improved K-means algorithm and attention mechanism are utilized to design the translation model of featured words. The model of this paper is validated and analyzed from two aspects, namely, performance indexes and application effect, respectively. In the six performance indexes, this paper’s model performs better compared to the other two control models. After the experience, the control group and the experimental group show a significant difference, i.e., the introduction of data mining algorithm is more effective in translating the featured vocabulary on the traditional model.

The development of artificial intelligence has brought new development opportunities for modern enterprises, but employees present a certain degree of resistance to the introduction of AI technology. The author tries to dissipate employees’ resistance and improve their acceptance of AI through organizational training. After researching organizational training and employees’ perceived awareness of AI, organizational training and employees’ acceptance of AI are taken as antecedent and consequent factors to construct a structural equation research model of the two. The research hypotheses are proposed based on the theoretical study of the two. Regression analysis of the effect of organizational training on employees’ AI acceptance is conducted through structural equations. The regression results show that training investment, employee motivation and knowledge training in organizational training all have a significant positive effect on both employees’ AI perceived ease of use and AI perceived usefulness. Employee AI perceived ease of use and AI perceived usefulness have a positive effect on employee behavioral intention to use AI for knowledge creation and automation. Employees’ behavioral intention to use AI for knowledge creation will have a positive effect on AI for knowledge creation, and behavioral intention to use AI for automation will have a positive effect on AI for automation.

New energy vehicles have a broad market, and the pricing and after-sales service of new energy vehicle enterprises have become the effective competitiveness of new energy vehicle enterprises. Therefore, this paper studies the pricing and after-sales service decision-making of new energy vehicles on the basis of game theory, and the study first gives a brief overview of game theory. Then, in the context of new energy vehicle subsidies, the optimal pricing under different sales modes is studied using game theory models. It also studies the utility of service stores of the same level of new energy vehicles with the support of game theory, and finally puts forward service suggestions from four aspects: optimizing offline service stores, expanding online services, developing service projects, and developing personalized services. This study can also provide valuable references for the pricing and service marketing of new energy vehicle enterprises, improve the competitiveness of after-sales service at the same time, and also put forward feasible suggestions for the future after-sales marketing methods of new energy vehicle manufacturers.

Achieving high-quality development has become the core essence of tourism industrialization, and is also a necessary step for the construction of ecological civilization to make new achievements. The article establishes the index system of China’s tourism high-quality development, and uses the entropy weight-TOPSIS model to measure the tourism high-quality development of China’s tourism in each region from 2013 to 2021. On this basis, it comprehensively applies density estimation, Dagum Gini coefficient and convergence modeling methods to analyze the regional differences and convergence of China’s tourism development. The study shows that the level of high-quality development of China’s tourism industry is gradually rising, and the regional differences in high-quality development of tourism are generally narrowing, with insignificant changes in intra-regional differences and narrowing of inter-regional differences, though. The overall trend of wave height in the central region is increasing, the wave height in the western region is decreasing and the width is increasing, and the wave height in the northeast region is increasing and the width range is decreasing. At the same time,  convergence coefficient shows that the gap between the level of high-quality development of tourism economy in the eastern, central and northeastern regions shows a trend of convergence, while the western region increases from 0.373 in 2012 to 0.388 in 2021, that there is no trend of convergence.

This paper synthesizes relevant theoretical knowledge and construction principles, selects 20 evaluation indicators to constitute the evaluation system, and divides the evaluation system into two subsystems in order to more intuitively demonstrate the relationship between international trade network optimization and regional economic synergy. Setting the source of research data, due to the initial data outline is not uniform, the research data for the dimensionless processing. Then the weight values of each index are calculated with the help of entropy weight method, and their values are substituted into the coupled synergy model of the fusion evolutionary algorithm. It is calculated that the synergy level of international trade network optimization and regional economy is medium in the period of 2014~2016, the coordination level of the two has been significantly improved in the period of 2017~2021, and the coordination level is good, and the coordination level of international trade network optimization and regional economy rises to excellent in the period of 2022~2023.

As an environmentally friendly and efficient public transport, the optimization of the operating frequency of electric buses is of great significance for improving passenger satisfaction and reducing operating costs. This paper proposes an optimal electric bus frequency setting method that combines LSTM prediction and two-layer planning. First, LSTM neural network is utilized to predict the passenger flow of electric buses. Second, a two-layer planning model is constructed, with the upper model aiming at frequency optimization and the lower model aiming at electric bus frequency setting. Finally, this two-layer planning model is solved by genetic algorithm to obtain the optimal electric bus frequency setting. The inbound and outbound passenger flow data of the 5th station of 363 electric bus in Q city are used for practical verification. The prediction results of the LSTM model on inbound and outbound passenger flow on weekdays and natural days are basically consistent with the actual values. The optimal frequency of 62 trips was solved using genetic algorithm. The maximum deviation of the actual capacity supply from the actual capacity demand curve is only 0.09% when the frequency setting is verified under the scenario of thousands of passenger flows. From the above analysis, it is shown that it is practical to design the optimal electric bus frequency using LSTM prediction and two layer planning model.