The development of digital technology provides more possibilities for the inheritance of Chinese excellent traditional handicrafts. This paper takes Chinese movable type printing as the research object, and develops and designs a user-oriented virtual experience system by combining its handicraft characteristics. In order to optimize the rendering of real-time images and video frames of the virtual scene in this system, this paper takes the deep learning oversampling algorithm as the basic framework, and uses two major types of neural network structures, namely convolutional neural network (CNN) and recurrent neural network (RNN), to carry out the rendering reconstruction, and at the same time, it uses the texture enhancement oversampling algorithm to recover the image texture details, improve the edge sharpness of the image, and comprehensively build the DLSS model. The performance of the DLSS model constructed in this paper and the virtual experience system of movable type printing is tested successively. The average score difference between the pre- and post-tests of the virtual experience system of this paper is 34.46, which is much higher than that of the traditional form of knowledge mastery of 20.76, indicating that the virtual experience system supported by this paper’s algorithms can effectively carry out the inheritance of traditional handicrafts.

Phishing has become an increasing threat on online networks with evolving Web, mobile device and social networking technologies. Therefore, there is an urgent need for effective methods and techniques used to detect and prevent phishing attacks. In this paper, a phishing detection model based on decision tree and optimal feature selection is proposed. An optimal feature selection algorithm based on a newly defined feature evaluation metric (f_Value), decision tree and local search is designed to prune out negative and useless features. The overfitting problem in the process of training neural network classifiers is mitigated. The optimal set of sensitive features for feature selection and the optimal structure for training the neural network classifier are constructed by tuning the parameters. Experiments on CART-based phishing detection system and comparative experiments based on different phishing detection models are also conducted. The experimental results show that the model precision, accuracy, and recall of the improved decision tree-based algorithm proposed in the article are 92.7%, 96.5%, and 88.3%, respectively, on the dataset of phishtank, and the three metrics are 98.3%, 99.1%, and 99.5%, respectively, on the datasets of Vrbanˇciˇc-small and show that the proposed CART has a higher performance than the many existing method models.

Image segmentation, as an important direction of computer vision, is gradually being applied to a variety of fields, however, the existing image segmentation methods still need to be improved in terms of segmentation accuracy and effect. In this paper, the variational level set method is used as the level set image segmentation method, and its theoretical basics and solution method (gradient descent flow method) are described in detail. For the problem of insufficient gradient vector flow in the traditional parametric active contour Sanke model, a global gradient vector flow model that can overcome the noise interference is given to obtain a more accurate gradient field, thus combining with the variational level set method to build an image segmentation model based on global gradient vector flow (GGF Snake). In the comparison experiments with three commonly used image segmentation algorithms, the DSC value of this paper’s algorithm reaches more than 96.00%, and the time used is less than 15s, which is better than the remaining three algorithms, and verifies the superiority of this paper’s algorithm.

Jiangnan gardens have become a valuable cultural heritage of China with its elegant garden style. The article proposes a binocular visual recognition system by analyzing the composition of the garden spatial elements and performing feature fusion based on scene-driven coefficients. Ablation experiments are conducted on each part of the constructed data enhancement framework for generating the design of the Jiangnan garden plan, which is applied to generate a set of high-quality datasets and apply the data to image segmentation for generating the design of the Jiangnan garden. The algorithm training is carried out by applying the generated design plan dataset. On this basis, the data from the actual Jiangnan garden research and the spatially quantized feature data are used to do the correlation analysis between the design elements and the aesthetic mood. The data enhancement framework constructed in this paper improves the IOU of ST elements to 0.537, and the average intersection and merger ratio MIOU is 0.389. It shows that the data evaluation framework based on visual recognition is suitable for the study of plan generation of Jiangnan gardens. The correlation coefficients of connection value, spatial control value, average depth value, and integration degree regarding aesthetic context with the data of Jiangnan garden design elements are 0.173, 0.301, -0.278, and 0.325, respectively, which indicate that there is a significant correlation between all of them.

Based on the concept of “user-centered”, this paper designs a product form optimization model based on ant colony algorithm. Through mining the online reviews of the products, we determine the perceptual imagery of users, and categorize the perceptual imagery and determine the weights from the perspective of user satisfaction. Combining the factor analysis of perceptual imagery and the contribution value of morphological features on perceptual imagery, the product morphology optimization fitness function is constructed. Solve the model according to the basic principle of ant colony algorithm, and study the decision-making method to assist product optimization. Take a brand A model forum word-of-mouth data as an example to analyze, obtain users’ perceptual imagery through SO-PMI algorithm, and assign values to perceptual intention weights with the help of cluster analysis. Determine the contribution value of morphological features through the SD investigation of product morphological differences. Genetic algorithm is introduced to carry out comparative experiments to verify the superiority of ant colony algorithm in optimizing model solving. Finally, the application effect of the predictive model solving scheme is analyzed through user satisfaction survey. The results show that the output of the product optimization design model based on ACO algorithm Model A is 8. 23.11% of the users are very satisfied with the optimized Model A, 65.55% of the users are satisfied, and 85.72% of the survey respondents are very willing and ready to buy the optimized Model A.

The evaluation of English course goal attainment is an important basis for colleges and universities to judge whether the goal of cultivating foreign language talents has been achieved. This paper proposes a method for quantitative assessment of course goal attainment according to the OBE concept. Calculating the importance of attributes about classification, the decision tree algorithm based on rough set is proposed, combined with association rules for deep mining of educational data. Collect quantitative educational data and questionnaire data of a university, modeling relying on SPSS Modeler 14.2, and outputting decision tree of influencing factors. Using the evaluation of course goal achievement to analyze the achievement of A4 course goals, and exploring the association rules of influencing factors based on the decision tree. The traditional decision tree algorithm is introduced as a control group to evaluate the performance of the rough set-based decision tree algorithm. The results show that the achievement degree of each sub-objective of A4 course is higher than 0.70, and students who have the achievement degree of A4 course objective greater than 0.7, the nature of their major is foreign language and they have passed the Grade 4 test have a higher possibility of achieving the final foreign language talent cultivation goal of the university. The precision of the assessment method based on rough set decision tree is maintained at about 88%, and the accuracy rate is basically maintained at about 90%.

Frequent outbreaks of cyanobacterial blooms in Lake Taihu are undoubtedly a great threat to the economic development of its neighboring areas and the safety of drinking water of its residents. This paper takes Taihu Lake as the study area and analyzes its geographic location information and development status. Then, based on the remote sensing data from MODIS and Landsat 8 satellites, the normalized vegetation index is improved to identify the blooms, and the dynamic detection method of cyanobacterial blooms is constructed by combining with the remote sensing inversion of water temperature. At the same time, the spectral performance of each band is integrated to excavate the characteristic information of cyanobacterial bloom, and the algorithm in this paper is used to process the satellite remote sensing data of cyanobacterial bloom in Lake Taihu to analyze its spatial and temporal distribution characteristics, which is used as the basis of the dynamic warning model for early warning. Then the LightGBM method is introduced to realize the all-weather spatial and temporal continuous monitoring of cyanobacterial blooms in Lake Tai. Analyzing the monitoring data of this paper’s model on the intraday change process of cyanobacterial bloom in Lake Taihu, it is found that the trend of intraday change in the area of cyanobacterial bloom in Lake Taihu in different seasons is relatively consistent, with the highest area of the bloom in autumn, accounting for 21% of the area of Lake Taihu’s water body. The study pointed out that after entering the fall, extra attention should be paid to the monitoring, prevention and control of cyanobacterial bloom in Lake Taihu.

In this paper, we design and implement a model network for English writing style generation using UNet network as well as ViT for encoding and decoding, and PatchGAN to enhance the identification speed. Based on the CRF-NLG model to identify and extract professional English terms, and design a special loss function to optimize the quality of writing style generation. The F1 value is used to evaluate the model recognition ability, and the writing style generation effect is explored by controlled experiments of the proposed model and three baseline models. The practical application results of the proposed model are visualized from four perspectives: overall evaluation, style strength, content preservation, and fluency, to verify its practical application effect. The results show that the proposed model exhibits the strongest performance in the two levels of content preservation and fluency, which are improved by 12.71% and 39.11%, respectively, compared with the existing GAN-based style generation model. Of the 119 modifications 92 (77.3%) were better, 17 (14.3%) were average, and only 11 (9.2%) were worse.

With the rapid development of science and technology, the traditional mode of teaching is inefficient and difficult to flexibly respond to the needs of knowledge updating, and generating content and applications based on AI has become an important way to solve this problem. According to the form of interaction in the digital exhibition hall, the article proposes SinGAN model and uses the multi-head self-attention mechanism to coordinate the overall features and detailed features in the generated adversarial network image, and to deal with the large range of dependencies in the image. The proposed AI-generated content and SinGAN image processing method are applied in the teaching of practical courses using the course “Digital Electronics Technology and Application” of a university in Guangdong Province, which specializes in electronic information and engineering, as an experimental object. The experiment shows that the percentage of content with a content quality score of 0.6 to 1.0 reaches 75.7%. As the course progresses, the keyword coverage rate reaches 0.996, and AI-generated content is efficiently applied in the course. The student performance of the experimental class with AI-generated content and image processing method teaching mode and the regular class with traditional teaching mode were 80.75 and 67.91 respectively, and the sample t-test for the significance of the student performance of the two classes was P=0.006, which showed a significant difference in the students’ performance between the two teaching modes. Students’ satisfaction with the new teaching mode is high, indicating that the AI-generated content and image processing methods proposed in the article have been well applied in education reform.

With the arrival of the big data era, the demand for massive data storage is growing, and distributed storage systems have become a key technology to solve this problem. The traditional HDFS system has a large storage overhead, this paper in order to improve the storage efficiency of massive data, the introduction of corrective deletion code (RS code) technology, to ensure the reliability of the data at the same time significantly reduce the cost of storage. In order to improve the storage efficiency of massive data, this paper introduces the corrective censoring code (RS code) technology, which ensures the data reliability and significantly reduces the storage cost. In addition, to address the problems of low coding efficiency and high repair overhead in the practical application of RS code, this paper further introduces the local repair code (LRC) technology, which reduces the data repair overhead, and compares and analyzes the application effect of optimization model (RS-LRC-HDFS). The experimental results show that after RS-LRC optimization, the time overhead of the HDFS storage system in the write process and read process is significantly improved by 81.12% and 93.01%, respectively, compared with the pre-optimization period, and the repair time of massive file data is reduced by 87.25%. It can be seen that it provides an efficient and reliable solution for massive data storage.