With the continuous development of the internet age, more and more art images are taking on digital forms, resulting in a new way of survival for art image digitization. However, the digitization process of art images is affected by various factors, resulting in poor results and low digital quality of art images. Therefore, this article conducted research on the digitization of art images based on metadata, and utilized BP (Back Propagation) neural network for metadata processing and analysis to achieve metadata visualization and interactive design. Animation production software was then utilized for image compression, transparent display, and modeling, and finally interactive display technology was used to display the dynamic design of art images. 4000 user feedback data and art image metadata from four age groups were collected and named A art image set. Starting from the visual communication effect, accuracy, and fidelity of art images, the differences in dynamic design of A art image digitization were compared. The experimental results showed that 2820 people were satisfied with the visual communication effect of dynamically designed art images, with a satisfaction rate of 70.5%. Only 1070 people in the control group were satisfied. The metadata accuracy of dynamically designed art images was greater than 80%, and the average accuracy was close to the median line, with small overall fluctuations. The deviation value between dynamically designed art image data and standard images is small, and the overall fidelity is relatively high. In short, the evaluation effect of digital dynamic design of art images is very good.

Short-term traffic speed prediction in Intelligent Transportation System (ITS) provides an important idea for solving traffic problems. To capture the spatio-temporal properties of traffic speed prediction, we proposes a Graph Convolutional Network-Gated Recurrent Units with Attention (GCN-GRUA) mode for expressway. The Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU) were used to extract the spatial and temporal features of traffic speed, and the attention mechanism was introduced to improve the prediction performance of the model. Experimental results from the real traffic data set of Qingyin Expressway show that the proposed model has a significant improvement in prediction accuracy compared with GCN, GRU and GCN-GRU models. In addition, the importance of speed characteristic variables and exogenous variables on the traffic speed prediction accuracy show that the speed data with the closest time interval has the greatest influence on the traffic speed prediction, followed by the daily cycle characteristics of traffic speed. As the prediction time increases, the relative importance of the velocity characteristic variable remains above 0.6, while the relative importance of the exogenous variable keeps rising.

As a new product of artificial intelligence, big data is widely used in daily life. Due to its appearance, people’s lives are more convenient and efficient, but at the same time, there are certain security risks, namely the leakage of private information, especially the financial information problem brought about by financial informatization has a more serious leakage problem. In order to effectively reduce the problems caused by the leakage of financial information privacy, this paper attempted to establish a model of related protection measures for financial big data information security by establishing a three-dimensional encrypted information model of big data or by using differential privacy method and using their own. The three-dimensional encrypted information model of big data overcame the defect that financial information is easy to be broken, while the differential privacy model overcame the defect of inaccurate protection of financial information, both of which can play a better protective role in different applications. The experimental results showed that in the process of accessing financial data information, with the increase of access frequency, the number of sensitive locations changes from 40 to 46. This also meat that a non-sensitive position becomes a sensitive position, which blurs the original sensitive position and achieves the effect of protecting the real sensitive position.

The expansion of the big data network has continuously increased the demand for student education and management, and the student education management system has been designed and developed, but the current student education management system does not take care of the core issues of management – teachers and students. As a result, the current management system cannot effectively manage student education information. In this paper, the biggest purpose of applying big data network to the design and development of student education management system is to solve management problems and improve education efficiency. The main algorithms involved in the network increase the development feasibility of the management system, and the design level of the student education management system is analyzed in order to provide a theoretical basis for the later design and development experiments of the management system under the big data network. Through experiments, it is found that the research on the design and development of the management system based on the big data network can effectively improve the quality of online education by 5.39%, which timely found the students who are left behind in learning, and actively followed up the students’ learning situation.

The development of economy is inseparable from the construction of traffic buildings. Especially in the current road construction, asphalt mixture is mainly used for pouring. Over time, the asphalt mixture is disturbed by other external factors, resulting in a decrease in the performance of the asphalt mixture. Under this background, this paper mainly studied the mesoscopic angle of the construction asphalt mixture through image processing technology, and analyzed the mesoscopic structural characteristics of the construction asphalt mixture. This paper took the void structure as the research index, and performs image enhancement, image denoising, image sharpening, image segmentation and image edge detection on the collected images of building asphalt mixture in turn. In terms of image enhancement, the image after histogram equalization is clearer in texture, distinct in layers and more prominent than the original image. In terms of image denoising, the median filter method is used, and the noise reduction effect is obviously better than other methods. In terms of image sharpening, the contour of the image sharpened by the Laplacian operator is clearer. In the aspect of image segmentation, the threshold segmentation method has obvious image void boundary and detail information, which is conducive to extracting void information. In terms of edge detection, the image lines under the Canny operator are complete, which greatly reduces the loss of edge information. On this basis, the void structure model was constructed and tested experimentally. The results showed that the average equivalent diameter, average perimeter, and average contour area of the voids in each layer have roughly the same trends as the layers increase. Not only that, the detected void ratio was about 8.14%, which was only 1.17% different from the actual void ratio. This showed that the void structure model constructed under the image processing technology has a significant effect on the porosity detection, and this result brought certain guiding suggestions for the follow-up study of mesostructure characteristics.

As a kind of humanistic culture, art has existed in people’s daily life long ago. Based on the research of the relevant literature in the art field, this paper found that there were some problems in the art field at present. Combining with the relevant problems, this paper proposed a remote storage system of art painting resources based on artificial intelligence. The system mainly included network security part and image scanning and recognition part. According to these two parts to achieve the purpose of safe remote storage of painting resources, this paper has carried out corresponding tests on the network security rate and image recognition rate of the system. Under the condition of ensuring the normal operation of the system, the data was compared and analyzed with the traditional painting resource storage method. The system method surpassed the traditional method in most performance with 100% security rate and 100% integrity rate. However, based on the particularity of the system method, it has not been accepted by most people at present.

With the application and development of generative AI technologies such as ChatGPT in the field of education and teaching, higher requirements have been put forward to improve the digital ethical literacy of pre-service teachers. However, there are still impediments to the current development of digital ethical literacy among pre-service teachers. Therefore, based on the social cognitive theory, this study aims to discuss the individual-level, behavioral-level, environmental-level, and social-level factors and their relationships that affect pre-service teachers’ digital ethics literacy. A total of 524 pre-service teachers in China were used as the study population. The study found that the factors influencing pre-service teachers’ digital ethics literacy include seven dimensions: personal values and digital ethics awareness at the individual level, digital ethics education competence and digital technology use skills at the behavioral level, resources and environment of the school and related educational policies at the environmental level, and social recognition at the social level. Among them, there are some interactions between the individual and behavioral dimensions, the environment and individual dimensions, the environment and behavioral dimensions, the individual, behavioral and environmental dimensions, and the social and individual behavioral dimensions two by two, and they play a positive influence on improving the digital ethical literacy of the pre-service teachers, but the interactions between the social and environmental dimensions are not significant. On this basis, the improvement of digital ethical literacy of pre-service teachers is discussed to provide some references for the related research on improving digital ethical literacy of pre-service teachers.

Along with the vigorous development of artificial intelligence in all walks of life, artificial intelligence technology has become an inevitable trend in the reform and development of physical education. First of all, this study takes constructivist learning, motor skill learning, and blended learning as the theoretical basis, and selects the three major goals of synergy, intelligence, and wholeness as the basis, and preliminarily constructs the theoretical framework of the health-promoting teaching (SCT) model for the professional disciplines of physical education in colleges and universities. Then, using the gray correlation model, the system characteristics behavioral sequence and the related factors behavioral sequence were established, and the similarity and dissimilarity of the development trends of the two sequences of factors indicator sequences were measured to confirm their correlation degree. Finally, through the implementation condition elements and equipment function elements obtained from the gray correlation analysis, the SCT model applicable to physical education courses was designed, and the effect of the application of the SCT model in physical education classes and its impact on the physical fitness of physical education students were explored. It is found that the SCT model is characterized by timely feedback, strong relevance and abundant resources, so the improvement of all the scores of the students in the experimental class is better than that of the control class, and the physical fitness of the students is also improved to a certain extent. It shows that the teaching effect of SCT mode is better than traditional teaching mode, and can be used in general multimedia classroom, which has certain universality and promotion value.

In order to meet the needs of high-quality development of the civil engineering industry, it is necessary to carry out corresponding teaching reforms in the level drawing course as a core basic course. The purpose of this paper is to explore whether the case teaching of leveling drawing can effectively improve students’ ability of leveling drawing. By analyzing the level drawing course, the case teaching method of level drawing is designed. Students of a higher vocational school were selected as the experimental objects, and the questionnaire survey was used to understand the current learning status of the students’ drafting and to carry out the teaching practice, and the statistical analysis method was used to explore the teaching effectiveness. After the teaching practice, the students’ learning attitude, skill mastery and teaching satisfaction increased by 46.24% as a whole, which was significantly different from the learning status quo before the practice (p < 0.01). Meanwhile, there was an improvement of 8.52% and 5.57% in learning achievement over the pre-practice and comparison students, respectively. The results indicate that case teaching of leveled drafting can effectively improve students' learning attitudes, develop students' skill mastery, enhance teaching satisfaction, and it has a significant role in promoting students' learning outcomes in leveled drafting. This study confirms the value of case teaching of plain drawing in professional practice and has positive significance for improving the quality of education.

This paper constructs the SAM agile iterative model according to the direction of online art course design, firstly by collecting art teaching related information and initiating the cognitive system of art teaching, and then entering into the iterative design phase to accomplish the development goal of the online art course incrementally through continuous iteration. Finally, after the double iteration phase, the software process enters the delivery phase to complete the design of the online art teaching course. The effect of the online art teaching course and its impact on delivery are analyzed in conjunction with the dynamic key-value memory network model based on the forgetting curve. The results of the memorization ability of art knowledge experiments in the pre-test have a mean value of 35.259, and the post-test has a mean value of 53.1254, while the Sig value of the paired test is 0.000, 0.000<0.05, which indicates that the effect of using the online course for art learning based on the Ebbinghaus forgetting curve is more significant on the learning of art knowledge than other applications. The regression results of the full sample model showed that overall instructors' use of big data aids for online art instruction significantly affects instructional delivery, t=1.245, P=0<0.05, which is significantly positive at the 1% level, indicating that the more adequate the use of these instructional methods, the higher the probability that students will rate their satisfaction with the instructional delivery.