The traditional Chinese culture contacted in history education has many common points with the Civic and Political Education, which has become a new method of value penetration of Civic and Political Education. This paper reveals the value penetration of traditional Chinese culture in Civic and political education from the perspective of innovative cultural topology, and puts forward three strategies to innovate the concept of Civic and political education, such as enhancing the effect of aesthetic connotation of Civic and political education. On this basis, variables are designed, structural equation model is constructed, and the role of teaching concept and other variables on the value penetration of traditional Chinese culture Civic and political education is analyzed through the reliability test and factor analysis. Combined with system dynamics, the system causality diagram is drawn according to the causal feedback relationship between internal and external factors to explore the causal relationship affecting the value penetration of Civic and Political Education, and then explain the mechanism of the role of traditional Chinese culture Civic and Political Education. It was found that all five paths among latent variables passed the significance level test of 0.001, and the teacher’s mission and ideal belief in teaching philosophy had the most significant effect on the value penetration of traditional Chinese culture Civic and political education, with path coefficients of 0.98. In the process of Chinese traditional culture civic education, it is necessary to reflect the unity of humanistic spirit and modern spirit, the unity of professional ethics and values, and to form the style of course civic education and course civic education characteristics with Chinese traditional culture.

At present, machine translation performs better in the general domain translation effect of large-scale bilingual corpus, but the translation effect in specific domains still needs to be improved. In order to optimize the accuracy of machine translation in the domain of English translation of professional terms, this paper proposes a translation model that incorporates syntactic knowledge and terminology. Aiming at the problem of more limited translation domain knowledge in the RNMT and Transformer models based on the self-attention mechanism, an optimization method is proposed. According to the domain characteristics of English translation of professional terms, English syntactic keywords are incorporated into the model training process, the special information contained inside the text of professional terms is learned, and the lexical properties of each word in the dataset are recognized before they are input into the model. Then attempts are made to incorporate the specialized terminology into the model to enrich the parallel corpus required by the model. The experiments confirm the excellent performance of the optimized translation model in this paper on the De→En terminology translation task, which improves 22.67 BLEU values compared to the base model. And the fluctuation of its BLEU value with the change of sentence length is small, which further indicates that the method optimizes the accuracy of the machine translation model in the English translation of professional terms.

Aiming at the needs of reconstructing the structure of calligraphic seal cutting strokes and virtual display, this study designs a GAN technique that integrates three models, namely, “WGAN, DCGAN and CGAN”. The Cycle GAN model is used to obtain the mapping relationship between learning and style migration by utilizing its cyclic consistency loss. Adaptive pre-morphing technique is introduced to process the input image to capture the outline information and morphological features of calligraphic seal carvings, and a Generative Adversarial Network-based Generative Model for Structural Reconstruction of Calligraphic Fonts (CRA-GAN) is proposed. Meanwhile, an online virtual display system is designed to provide users with a good sense of experience in the virtual display of calligraphy. The results show that the CRA-GAN model can better capture the details and global information of the fonts, and its recognition rate of the eight calligraphic fonts ranges from 90.42% to 97.38%, and the MOS rating value of the text image is > 8.5 points, and its recognition results are in line with the observation characteristics of the human eye for calligraphic images. The FID calculation result of the CRA-GAN method ( 204.361) of the CRA-GAN method is much lower than that of other methods, which obviously improves the diversity and visual quality of the generated calligraphic fonts. This paper evaluates the user’s experience of the system from five aspects: narrative experience, emotional experience, sensory experience, cognitive experience and interactive experience, and calculates that the final score of the system is in the range of 80-100, which indicates that the user’s satisfaction is very high after actually experiencing the virtual display system.

Financial performance optimization is an important embodiment of enterprises to improve operational efficiency and optimize management level. The article proposes a method of financial performance optimization and evaluation using group intelligence algorithm in order to optimize the financial performance of enterprises. EVA is introduced to establish the evaluation index of enterprise financial performance. The financial performance prediction model is constructed according to the propagation process of BP neural network, and the IPSO-BP algorithm is utilized to avoid BP from falling into local optimum and improve the prediction accuracy. In the learning ability test, the relative errors of the EVA value, EVA payoff and EVA rate of the IPSO-BP algorithm are controlled within 6%, 8% and 10% respectively, and the average relative error of the model application results is 3.87%. The model in this paper can achieve more accurate financial performance assessment and prediction, which is conducive to the optimization of financial performance management of enterprises.

The problem of English education quality is worth exploring in depth, and quantifying the indicators of English education can help to understand the problems in teaching and improve the quality of teaching. The study firstly establishes the English education quality evaluation index system, including five first-level indexes of teaching resources, teaching content, teacher quality, teaching effect and teaching quality feedback and 15 second-level indexes, such as network resources, book resources and comprehensive teaching content. On this basis, the combination weights are determined by fusing the G2 method and the projection tracing method through the combination assignment method to eliminate the one-sidedness problem of adopting a single assignment method, and then the cloud model theory is introduced to establish the English education evaluation model based on the cloud model. Problems and shortcomings of multi-objective linear programming weight allocation in English education evaluation system are found through the evaluation results, which lead to low multi objective linear programming weight allocation in English education evaluation system.

In order to enable ships to operate stably for a long time under complex sea conditions, all kinds of ships have an urgent need for gyroscopic rocking reduction devices. This paper takes the double gyro rocking reduction device with better rocking reduction effect as the research object, establishes its corresponding nonlinear dynamic equations, adopts the energy method to establish the differential equations of motion, and deduces the dynamic model of the rocking reduction double gyro. A parameter optimization model is established with the main objective of improving the shaking reduction effect, and the key components of the shaking reduction double gyro are optimized. The bacterial foraging optimization algorithm is selected to solve the model, and the multi-objective parameter optimization model is established. For one to five wave classes, the middle value of the wave height of the meaningful wave is selected for the dynamic simulation experiment of the double gyro. When the wave level is less than three time level, the rocking reduction performance of the rocking reduction double gyro reaches 87.5%, 78.1% and 77.78%, respectively, and the transverse rocking reduction performance is good. Under the simulation environment of sea state I (wave height 2.5m, average period 7s) and sea state II (righteous wave height 2.5m, average period 12s), the rocking reduction efficiencies of the ship after parameter optimization are improved by 6.44% and 10.09%, respectively.

With the rapid development of computer vision technology, image enhancement technology involves an increasingly wide range of research content. At the current stage, picture hierarchy enhancement technology is a research hotspot in the field of image enhancement. This paper proposes an oil painting image enhancement network based on positive probability distribution guidance. The multidimensional spatial information of the samples is obtained through the multibranch information extraction architecture in the network structure, and the probability distribution estimation module estimates the probability distribution through the obtained multidimensional spatial information. In addition, a new image enhancement method based on the RGB color balance method is proposed, which combines the multi-scale Retinex enhancement algorithm with color recovery and the RGB, Lab color space histogram adaptive stretching algorithm, to further improve the effect of oil painting image display. The experimental results show that the method has a better image color bias correction effect compared with the existing techniques. In terms of subjective evaluation, the average subjective score of this paper’s method in three different aesthetic levels reaches 9.15, obtaining a high evaluation. The samples enhanced based on this paper’s algorithm all obtained high aesthetic index scores, indicating that the oil paintings under this paper’s algorithm are in line with the public aesthetics, which is of great significance to the work of oil painting artists.

AI technology can accurately capture and feedback user emotions in digital media interaction to realize precise interaction. In this paper, we design an AI emotion interactivity enhancement model based on multimodal fusion, and apply the neural network model of Bi-GRU and dual attention mechanism to fuse the long and short-term emotion classification results of the tested samples at the decision level to obtain the final emotion classification results. Then the weight coefficient vector of each sentiment category is calculated based on the sentiment classification confusion matrix of the classifier, which is used as the a priori knowledge for multimodal sentiment analysis for decision fusion. The performance is examined on the MOSI dataset and the AI-based interaction design strategy in digital media is proposed. Analyzing the interaction design effect, the interaction design applying the model of this paper has better user experience sense, emotional arousal, pleasure level, and emotional feedback effect in subjectivity evaluation than the control group, and 75% of the experimental subjects think that the feedback-adjusted digital media has a better pleasure level.

In the era of artificial intelligence, human-computer collaborative teaching has become a new picture of future development in the field of education. Based on the theory of human-computer collaboration and the theory of production-oriented approach (POA), this paper constructs a university English POA teaching model based on human-computer collaboration. It also combines the speech recognition algorithm, S-T behavioural analysis method and social network analysis method to conduct a case study on the current situation of college English classroom teaching under this instructional design model. Meanwhile, a teaching experiment is designed to verify the effectiveness of the constructed POA teaching model. The results of the case study show that most of the university English courses favour the lecture mode, with less interaction between students, and the classroom is dominated by teacher lectures and teacher-student interactions, but at the same time, many teachers begin to experiment with the discussion mode, which increases teacher-student interactions and student-student interactions in the classroom. In addition, the experimental group adopts the POA teaching mode and the control group adopts the traditional lecture mode, and its independent samples t-test results show that the experimental group is significantly better than the homogeneous control group in the dimensions of interest, ability, attitude, and test scores in English literacy after the experiment (P<0.05), which suggests that the combination of AI technology and the production-oriented method can effectively improve the effectiveness of the design of university English literacy teaching and achieve better teaching effectiveness and has potential application value.

Market economy is characterized by the uncertainty of supply and demand, so enterprises can realize the optimization of inventory cost control only by reasonably forecasting the demand of supply chain. This paper studies a supply chain demand forecasting method based on machine learning. The factors affecting supply chain demand are collected and analyzed, and the ARMA model, which combines autoregressive model and moving average model, is used to forecast supply chain demand. Then, through the introduction of procurement cost, storage cost and time cost, a multi-level inventory model is established, and the immune genetic algorithm is used to solve the model to find the optimal inventory cost. The experimental results show that the prediction model has good forecasting performance. After using the optimized scheme, the total inventory cost of the enterprise supply chain is reduced by 17.35% and 13.69% respectively. It can be seen that, on the whole, the method in this paper has a good effect of supply chain demand forecasting and cost control.