In this paper, the semantic description framework is used to standardize the extraction of semantic information of non-legacy images. The SIFT algorithm is chosen to calculate the key feature points of non-legacy images. The integrated semantic description framework and SIFT algorithm construct a model to extract the non-heritage image features globally and process them locally, and add the attention feature fusion module to fuse the features that are inconsistent in semantics and scale, so as to realize the accurate extraction of features. Use the algorithmic model of this paper to extract the color features of She ribbons. Develop a website for she color band design and verify its usability. Collect website evaluation data from target users to study the role of digital translation of non-heritage elements. The color feature extraction results are richest and most detailed when the number of She ribbon feature colors extracted is 21. The website usability scale score was 50.31, rating B+, with usability. 65% of the users thought that the website embodied the cultural characteristics of She ribbons. 71% of the users thought that the website was very helpful for understanding the ethnic graphic culture. 88.16% of the users thought that the digital design of She ribbons could effectively promote the dissemination of the ethnic graphic culture.

Increasing urbanization has led to large changes in residents’ consumption behavior, but due to a variety of factors, the overall level of residents’ consumption is low, so that it cannot play its role in promoting economic growth. This paper selects the panel data of China Household Finance Survey from 2011 to 2019, and empirically analyzes the impact and path of changes in residents’ consumption behavior on consumption upgrading by constructing structural equation model and fixed effect model combined with STATA software. The study shows that changes in residents’ consumption behavior and its dimensions can promote residents’ consumption upgrading, and there is regional heterogeneity in the impact of changes in residents’ consumption behavior on residents’ consumption upgrading, and its promotion effect on residents’ consumption upgrading is stronger in the developed regions in the Middle East. Based on the above findings, this paper puts forward feasible suggestions on how to optimize residents’ consumption behavior to better promote residents’ consumption upgrading.

This paper takes the integration of AI technology into piano teaching as the starting point, generates accompaniment rhythms through AI computation, adopts deep learning model to generate accompaniment, and builds a multi-level accompaniment effect generation mechanism. Taking the MuseFlow model as the base model, the generative adversarial network and variational autoencoder are introduced to optimize the structure in a limited arithmetic environment. Quantitative and manual evaluations are used to measure the accompaniment generation effect of the proposed mechanism, and controlled experiments are designed to explore its practical application effect. The results show that the improved MuseFlow model generates accompaniment with an average pitch distance of 0.92, which is 0.15 smaller than that of MMM, and the overall score reaches 4.18. The scores of the experimental group in all six abilities are significantly higher than those of the control group, the degree of students’ positive response to each ability increases to some extent, and the number of students who consider the ability of melodic creation to be at a satisfactory level is 18 more than that of the pre-experiment after the experiment.

Network teaching has become an important way of teaching reform in current higher education and has been applied in the education of various courses. This paper proposes a kind of intelligent auxiliary teaching system based on P2P mode, and researches the realization of the system with the example of Civics course. The construction of “Civics Course Teaching Evaluation System” is systematically discussed by using the fuzzy comprehensive evaluation method, and the weights of the indicators are calculated by entropy weighting method and hierarchical analysis method. Taking the teaching of Civics and Political Science in a university in Guangdong Province as the research object, the intelligent teaching system proposed in this paper is applied to evaluate the interactive effect of teaching with the evaluation system constructed in this paper. The evaluation analysis shows that the school’s evaluation results of all indicators are above 80 points, and the overall teaching rating of its Civics and Political Science course is 86.33, in which social merit, teaching equipment, teaching expression, and professional ethics have the highest scores of 94.37, 92.32, 89.02, and 88.52, respectively. It shows that the intelligent auxiliary teaching system for Civics proposed in the article is well applied in actual teaching.

Aiming at the bridge project in the construction of the development of the status quo of the overdevelopment, maintenance and management level lagging behind, this paper, under the premise of ensuring the safety of the bridge, the bridge surveillance monitoring and risk early warning launched a study to solve the problems of its operation and repair and maintenance. For bridge monitoring and safety monitoring, this paper is based on the vibration acceleration of bridge structure damage identification. On this basis, the damage recognition model constructed by using common neural networks convolutional neural network (CNN), long short-term memory network (LSTM) and deep autoencoder (DAE), and the recognition effect of the three models is compared. This for, for the bridge risk problem, this paper utilizes the Extreme Learning Machine (ELM) and Firefly Algorithm (GSO), constructs the implementation of the GSO-ELM algorithm model for early warning of the bridge safety risk, and the experimental results show that the model proposed in this paper has good effect, which provides support for the future development of the bridge structural safety facilities should be developed in the direction of digitization, automation, and networkization.

A scientific, comprehensive and effective evaluation system of asset management performance of public colleges and universities in the context of high-quality development in the new era contributes to the “asset power” for the construction of high-level and high-quality development of colleges and universities. This paper takes 20 public colleges and universities in Province Y as research samples, and analyzes the asset management performance of public colleges and universities and its influencing factors through the super-efficiency DEA model and SFA model. The results show that the asset management performance of 13 public colleges and universities has reached DEA effective, and the rest of them are DEA ineffective. Human and material inputs have a significant positive effect on the asset management performance of public universities in terms of inputs, and both research income and number of patents have a significant positive effect on the asset management performance of public universities in terms of outputs at the 1% level. Relying on the scientific evaluation index system of asset management performance of public universities, establishing a high-level asset management team and clear budgeting and audit management are effective means to improve the asset management performance of public universities.

With the rapid development of the sports industry, quality traceability and credibility issues have become important issues in the sports industry chain. Traditional quality traceability and credibility assurance methods have shortcomings in efficiency and accuracy, and emerging technologies need to be adopted to solve them. Blockchain technology is considered an important means to address quality traceability and credibility issues in the sports industry chain due to its decentralized, transparent, and tamper resistant characteristics. This article proposed a quality traceability and credibility assurance system based on blockchain technology to address the issues of quality traceability and credibility in the sports industry chain. The system adopted blockchain technology to achieve quality traceability and information credibility assurance in the production, circulation, and consumption processes of sports products. The system adopted distributed ledger technology to record the production, circulation, and consumption records of products, and achieved automated quality inspection and transaction verification through smart contracts. The experiment in this article showed that using this system can improve efficiency and reliability by 80% -95%. The research on methods and systems for enhancing the credibility of quality traceability in the sports industry chain through blockchain can effectively improve the quality traceability ability of the sports industry chain, thereby safeguarding consumer rights and market stability.

As the economy develops, the tourism ecological environment (TEE) has been gradually damaged. The ecological environment is the basis of human life, and the sustainable development of the ecological environment is of great importance to promote the stable development of society. China has rich grassland tourism resources. However, as a result of the rapid development of tourism, some scenic spots have been overdeveloped and commercialised, leading to the destruction of natural landscapes, damage to ecosystems and the gradual sanding of large tracts of grassland. The desert grassland used for tourism development is located between the grassland and the desert, and is the barrier that ensures the entire grassland ecosystem. To carry out environmental management of tourism ecology, it is necessary to construct a statistical monitoring index system for tourism ecology. However, traditional ecological environment monitoring is mainly based on manual sampling survey, which is cumbersome. The monitoring data is not accurate enough. In this paper, remote sensing technology (RST) was used to obtain remote sensing images of desert grassland, and intelligent image processing (IIP) technology was used for feature recognition. Compared with the traditional ecological environment statistical monitoring method, it showed that: In desert grassland A, the average monitoring accuracy of the traditional ecological environment statistical monitoring method and the ecological environment statistical monitoring method based on IIP were 90.12% and 94.56% respectively; in desert grassland B, the average monitoring accuracy of the traditional ecological environment statistical monitoring method and the ecological environment statistical monitoring method based on IIP were 88.20% and 92.60% respectively. Therefore, statistical monitoring of TEE based on IIP can improve the monitoring accuracy of ecological environment indicators.

In order to analyze the reading behavior and its meaning of readers in blockchain online reading platforms, this article conducted research on reading emotion recognition. This article utilized the characteristics of blockchain technology to analyze the reading mode of blockchain internet platforms. By using audio and image bimodal recognition methods, the recognition of readers’ reading emotions can be achieved. After feature extraction of speech and facial images, hidden Markov models (HMM) can be used for speech emotion recognition. Support vector machines (SVM) can be used for facial image emotion recognition, and decision level fusion can be used for bimodal emotion recognition. This article obtained the final emotion recognition results to analyze and predict user reading behavior. Analyzing the psychological state of readers based on emotional recognition results can achieve more intelligent reading information push. Experimental results on the effectiveness of reading bimodal emotion recognition showed that the accuracy of reading bimodal emotion recognition based on decision level fusion was much higher than that of single modal emotion recognition. The bimodal method has an average accuracy rate of over 85% in emotion recognition and has a high effect in emotion recognition. Reading bimodal emotion recognition based on audio and image can accurately identify readers’ emotions, adjust information push content in a timely manner, and achieve the regulation of readers’ emotions, which has high application value.

At present, there are differences in the building of information in various career institutions. The degree of implementation of management, teaching and services is uneven, and educational resources are limited and unevenly distributed. The construction of educational resources includes the overall layout, structure and quantity of resources, information mode, service impact, etc, all of which require systematic planning. Under the above background, this paper conducted research on the topic of building a model of co-construction and sharing of digital ideological and political resources for embedded courses based on artificial intelligence algorithms, and considered the insufficiency of the existing digital ideological and political resources in the allocation efficiency and insufficient system sharing, as well as creatively used artificial intelligence algorithms to improve the previous system. In the algorithm, the texture mapping of the system was carried out, and the duty cycle of each columnar area was specified. In the experiment, the number of resources in the digital resource platform was investigated, and the input of different types of colleges and universities in digital ideological and political resources was collected. The explanation of experimental data: 83% of 985/211 colleges and universities used the database designed in this paper, and 17% of them actively built the database; 57% of the general undergraduate schools used the database designed in this paper, and 20% were under construction, as well as 13% were still preparing. This showed that in general undergraduate schools, a small proportion of the digital ideological and political resource sharing model was used, and the 985/211 colleges and universities had relatively good investment in the construction of digital ideological and political resources.