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Under the background of the Internet, the effective use of new media means can improve the marketing level of rural tourism and adapt to the development needs of the new era. The construction of a new media marketing platform for rural tourism uses Hadoop, an extensive data framework system, to analyze the tourism preferences of various consumers. Based on the constructed platform, a rural tourism consumer portrait model is designed using the fuzzy-C mean clustering algorithm and the personalized recommendation algorithm considering the time effect to realize the precise marketing of rural tourism consumers. In the analysis of consumer precision marketing, consumers can be subdivided into three categories of growing, high-value and general consumers through clustering, and the marketing success rate of the recommendation algorithm in this paper reaches 0.765. The regression analysis further understands that the quality of the products and services of the rural tourism website (0.586) is the factor that has the most significant influence on the satisfaction of the consumers, followed by brand awareness (0.556), and that the different new rural tourism media marketing strategies have the most significant influence on the satisfaction of consumers. Consumer satisfaction is affected by media marketing strategies in other ways. Based on the study’s results, this paper proposes relevant optimization of new media marketing strategies for rural tourism to promote sustainable development.

In this paper, we use the Hanning window function to add windows to the piano accompaniment human brain perception signal, and the noise and redundant features in the piano accompaniment human brain perception signal are downgraded by principal component analysis and Relief algorithm for dimensionality reduction processing and feature selection. Based on the basic structure of RCNN, the ReConv-EEG Net EEG identity recognition method is established, and the feature vectors in the recurrent network are classified and computed through the feed-forward layer, the normalization layer and the output layer to obtain the feature values of the EEG signals. Under different speed accompaniments, the subjects perceived the high-speed piano accompaniment with the lowest α -wave power spectral value of 47.85 on average, while the original speed accompaniment had the most significant α -wave power spectral value of 49.12 on average.Meanwhile, there were differences in the perception of piano accompaniment among subjects, and the mean EEG δ -wave synchronization indexes of the non-musicians and the musicians differed by an average of 0.2297 when they were listening to the same accompaniment.The method in this paper contributes to developing a new method for deep learning-assisted music perception research in the field. Also, it lays the data foundation for applying the technique to the field.

Textile and clothing art patterns are the basis of clothing design, and modern clothing design is moving towards intelligent development. In this study, the VGG19 network is first used to construct a style migration model based on attention mechanisms to realize image style conversion for the intelligent generation of textile and clothing art. The keying algorithm is optimized using a convolutional network to segment textile and clothing art images and produce more transparent images. In using textile and clothing art patterns, the signal-to-noise ratio of the style migration model in this paper is between 9.39 and 25.53 dB, and the structural similarities are all above 0.7. The average score of its visual effect is 4.2, and the scores of each dimension of pattern generation and the scores of the five aspects of consumers are all above 4. The style migration model constructed in this paper has excellent performance in the use of textile and clothing art pattern design, which provides an effective way for the use of style migration network in the field of textile and clothing art patterns, and improves the efficiency of textile and clothing art pattern design and creation.

The unique style of early works of fine art depends on the infinite derivation of the image factor symbols metaphorically behind them, so the symbolic interpretation of early works of fine art is a search for the missing beauty of contemporary art. In this paper, we start from multi-scale association rules, use Gaussian pyramid and cubic convolution methods to extract image metaphor features in fine art works, and weight the features. Based on Putschke’s emotion classification, a multimodal metaphor dataset is constructed. The symbolization method is used to represent the symbolic emotions in the art works, and then the bidirectional Bi-LSTM model is used for recognition. On this basis, using the model constructed in this paper, the symbolic and imagery features of art works are analyzed, and the symbolic techniques of art works are interpreted from three perspectives: brushstroke, color, and spatial metaphor. Structural metaphors in fine art works contain 40% neutral emotions, followed by 36.4% positive emotions. Stroke A has a higher mean score at 3 and 4 line sample points, respectively, of -1.1646 and -1.1106, and the emotions triggered by these two line samples are more significant. Interpreting early art works can help enhance the aesthetics of contemporary art for modern audiences.

The development of virtual reality technology promotes the progress of medicine and has a pivotal role in dentistry. This paper focuses on collision detection and force feedback haptic perception for oral virtual surgery. The study uses a combination of octree and AABB enclosing box for collision detection, and a force feedback computation method based on meshless SPH to construct a force feedback model after collision detection for oral virtual surgery. This paper’s octree AABB structure algorithm achieves the shortest time in collision detection experiments, with a 29.82% and 18.03% increase over the traditional AABB algorithm and binary tree structure, respectively. The accuracy of the feedback force in this paper has been improved by 2.18%, which is closer to the operation effect of actual clinical surgery. The model presented in this paper has specific feasibility and accuracy, and can be used for collision detection and force feedback sensing in oral virtual surgery.

Modern and contemporary literature in colleges and universities is a deeply learned and traditional humanities subject, and the development of Internet technology has a different impact on the form of teaching literature. This paper combines the MOOC platform for information technology with the teaching of modern and contemporary literature and establishes a blended teaching mode for both modern and contemporary literature. Aiming at the problem of personalized recommendation of literature teaching resources in blended teaching, this paper introduces the singular value decomposition to improve the collaborative filtering algorithm in order to meet the students’ efficient learning needs of current and contemporary literature. After the teaching experiment of the blended teaching mode of current and modern literature for the students of the class of 2021 in the sample universities, the reading breadth score of the students in the experimental group in current and contemporary literature reaches 3.72±0.61. The reading ability of the two groups of students shows a significant difference at the 1% level. Furthermore, students in the experimental group were 0.3 points more motivated to learn modern and contemporary literature than those in the control group. The integration of current and modern literature teaching with information technology can promote students’ learning interests and improve the quality of current and contemporary literature teaching.

This paper screens and calculates the factors affecting the quality of Japanese translation through the gray correlation analysis method. First, the Pearson correlation coefficient is utilized to explore the correlation between each factor and translation quality, and the multicollinearity between the influencing factors is tested. A quadratic test was conducted on the influencing factors using Lasso variable selection and ridge regression methods to eliminate those with linear correlation. On this basis, Japanese translation skills are designed and practiced from the perspectives of naturalized and alienated translation strategies to address cultural differences. The correlation coefficients of the factors affecting the quality of Japanese translation ranged from 0.7017 to 0.9309. The correlation coefficient between translation integrity and average sentence length was 0.491. The overall rating of the translated texts of each student reached a maximum of 97.87 points after using the translation technique. The translation technique in this paper is more effective in Japanese translation, which provides a reference method for the translation strategy under the perspective of cultural differences between China and Japan, and also helps to effectively convey the country’s culture.

With the help of the dynamics model of the integration of AI and the management of college civic education, this paper investigates the integration of artificial intelligence and civic education in three directions: application specification, application design, and application practice. Using differential equations to represent the non-linear dynamics equations of the system and by means of differential solutions, the evolution mechanism of AI education application and integration is projected in this paper. The integration of AI and college civic education is explored through empirical analysis and comparative analysis. The results show that the average value of the comprehensive score of the three directions in the sample colleges and universities is 77.5828, which is in the bare integration stage, and the integration degree in the “formulation” path is the best, which is 85.698, and is in the complete integration stage. The students in the experimental class of the sample universities scored better than the control class without AI integration in civic education. There were no students with scores below 60, and there were 32 more students than the control class in the 90–100 segment.

Labor education in colleges and universities in the new era is the fundamental task of implementing the principle of establishing morality and educating people, as well as an essential hand in cultivating college students with all-round development of morality, intelligence, physicality, aesthetics, and labor. This paper focuses on the objective evaluation method of college students’ labor education to conduct research. Using the improved binary tree support vector machine multi-class classification algorithm, an evaluation model for college labor education based on a binary tree support vector machine has been constructed, and an evaluation system for labor education has been developed. The results show that the majors that have compulsory labor education courses, elective labor education courses, and have added labor education modules in their major courses are all art and sports, accounting for more than 50% on average. The percentages of liberal arts, science, economics and management, art and sports, medicine and science, and agriculture, which did not offer labor education-related courses, were 26.35%, 22.16%, 19.91%, 13.59%, 11.03%, and 13.19%, respectively. It is evident that labor education courses are not distributed equally in colleges and universities, and there are still some majors that do not offer labor education courses. In terms of labor education, most of the labor education through Civics and Political Science classes is provided to students. Managers are those who choose the most specialized courses. Students also tend to participate in bicultural education and social practice activities, accounting for 29.02% and 43.01%, respectively. It shows that the algorithm in this paper can objectively and effectively evaluate labor education in colleges and universities and identify the deficiencies in education.

With the advancement of the information age, the traditional teacher-led English classroom interaction in colleges and universities has made it challenging to meet the individualized needs of different students. Therefore, this paper takes the current classroom behavior data of college students as the basis, uses the K-means clustering algorithm and I-divergence measure to categorize, analyze, and study the student behavior data, and digs out the correlation between students’ classroom behavior data and teaching interaction. This paper takes all the teachers and students of English majors in college M as the research samples. It conducts experimental research in terms of teachers’ and students’ classroom speech rates, teachers’ and students’ questioning rates, and teachers’ and students’ use of instructional technology, etc. The 10 classes of English majors in college M do not have more than 50% of the students’ speech rate, except for class 2. Class 2 has the highest speech rate (61.64%) among students, while class 5 has the lowest rate (7.5%). From the point of view of active questioning rate, 10 classes had very low SQ ≤ 0.02, and even in classes 1, 3, 4, 5, and 6, there was a situation where students’ active questioning rate was zero. After analyzing the student behavior data in some classes, it was found that the mean score of English test students in the experimental group before and after the test increased by 4.687, corresponding to a significance Sig value of 0.000