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This study proposes a novel artificial intelligence (AI)-assisted design model that combines Variational Autoencoders (VAE) with reinforcement learning (RL) to enhance innovation and efficiency in cultural and creative product design. By introducing AI-driven decision support, the model streamlines the design workflow and significantly improves design quality. The study establishes a comprehensive framework and applies the model to four distinct design tasks, with extensive experiments validating its performance. Key factors, including creativity, cultural adaptability, and practical application, are evaluated through structured surveys and expert feedback. The results reveal that the VAE + RL model surpasses alternative approaches across multiple criteria. Highlights include a user satisfaction rate of 95%, a Structural Similarity Index (SSIM) score of 0.92, model accuracy of 93%, and a loss reduction to 0.07. These findings confirm the model’s superiority in generating high-quality designs and achieving high user satisfaction. Additionally, the model exhibits strong generalization capabilities and operational efficiency, offering valuable insights and data support for future advancements in cultural product design technology.

In recent years, artificial intelligence (AI) technology has rapidly advanced and found widespread application in corporate management. Leveraging AI to enhance Environmental, Social, and Governance (ESG) performance and promote sustainable development has become a focal point for both academia and industry. This study aims to explore the impact of AI-driven ESG practices on the sustainable development performance of central state-owned enterprises in China. It analyzes the specific effects of AI technology in corporate governance, environmental protection, and social responsibility, and evaluates its contribution to the overall sustainable development of enterprises. The study employs a survey method, targeting 200 managers and employees from Central state-owned enterprises. The questionnaire comprises 15 questions covering three dimensions: corporate governance, environmental protection, and social responsibility. Descriptive statistics and correlation analysis are used to conduct an in-depth analysis of the collected data. The results indicate that respondents positively assess central state-owned enterprises in terms of corporate governance, environmental protection, and social responsibility, with particularly strong performance in social responsibility. Additionally, a regression analysis model is constructed. The results demonstrate that AI technology can enhance the practices and foster the sustainable development of central state-owned enterprises. Furthermore, ESG serves as a mediating factor between AI adoption and improvements in sustainable development performance. The findings provide practical insights for improving corporate management efficiency, enhancing environmental performance transparency, and boosting social image and brand value.

Due to complex environmental conditions and varying noise levels, traditional models are limited in their effectiveness for detecting anomalies in video sequences. Aiming at the challenges of accuracy, robustness, and real-time processing requirements in the field of image and video processing, this study proposes an anomaly detection and recognition algorithm for video image data based on deep learning. The algorithm combines the innovative methods of spatio-temporal feature extraction and noise suppression, and aims to improve the processing performance, especially in complex environments, by introducing an improved Variable Auto Encoder (VAE) structure. The model named Spatio-Temporal Anomaly Detection Network (STADNet) captures the spatio-temporal features of video images through multi-scale Three-Dimensional (3D) convolution module and spatio-temporal attention mechanism. This approach improves the accuracy of anomaly detection. Multi-stream network architecture and cross-attention fusion mechanism are also adopted to comprehensively consider different factors such as color, texture, and motion, and further improve the robustness and generalization ability of the model. The experimental results show that compared with the existing models, the new model has obvious advantages in performance stability and real-time processing under different noise levels. Specifically, the AUC value of the proposed model is 0.95 on UCSD Ped2 dataset, which is about 10% higher than other models, and the AUC value on Avenue dataset is 0.93, which is about 12% higher. This study not only proposes an effective image and video processing scheme but also demonstrates wide practical potential, providing a new perspective and methodological basis for future research and application in related fields.

This study proposes a stock price prediction model based on the Residual-enhanced Channel-Spatial Attention Network (R-CSAN), which integrates channel-spatial adaptive attention mechanisms with residual connections to effectively capture the multidimensional complex patterns in financial time series. The R-CSAN adopts an encoder-decoder architecture, where the encoder extracts feature correlations from historical data through multiple layers of channel-spatial attention modules, and the decoder incorporates a masking mechanism to prevent future information leakage and introduces a cross-attention mechanism to model inter-market correlations. Experiments conducted on four cross-market stock datasets, including Amazon, Maotai, Ping An, and Vanke, demonstrate that R-CSAN significantly outperforms not only traditional baseline models such as ARIMA, LSTM, and CNN-LSTM, but also recent Transformer-based approaches like Informer, Autoformer, and iTransformer on metrics including RMSE, MAE, MAPE, , and return on investment. The model reduces RMSE by 17.3–49.3% compared to traditional methods and 6.2–11.6% compared to Transformer variants, with the highest reaching 93.17% and an increase in return on investment to 482.64%. Ablation experiments confirm the critical contributions of each component, with the temporal module removal causing an average increase of 38.6% in RMSE and channel-spatial attention removal resulting in a 21.3% increase. Moreover, the model provides an interpretative analysis of features and temporal dimensions through attention weight visualization, offering insights into both indicator importance and critical time periods for prediction. In practical applications, R-CSAN’s outputs can be integrated into quantitative trading strategies including breakout trading, moving average crossover signals, and portfolio allocation optimization, providing a new paradigm for robust prediction in highly volatile markets.

This work aims to delve into digital promotion strategies of the musical intangible cultural heritage on the TikTok short video platform and their impact. A comprehensive theoretical framework for questionnaire analysis is constructed by drawing on theories such as Digital Communication Theory, Uses and Gratifications Theory, Lasswell’s Five Factors Communication Theory, and Stimulus Organism Response theory. Subsequently, a questionnaire survey is conducted on participants’ perceptions of digital platforms, evaluations of promotional methods, and the role of digital communication in learning and engagement. This aims to gain insights into their awareness of musical intangible cultural heritage and their perspectives on digital communication. Finally, the data from the questionnaire are organized and analyzed. The findings suggest that users generally have a positive overall evaluation of digital promotion channels. They particularly praise user engagement and utilization of TikTok special effects and sound. In addition, respondents demonstrate a widespread awareness of musical intangible cultural heritage content on digital platforms, with the majority indicating “very familiar” and “relatively familiar”. Regarding promotional effectiveness, digital communication significantly contributes to increased attention, awareness, dissemination effects, expanding audience demographics, and enhancing engagement. This work provides empirical support for the promotion of musical intangible cultural heritage on digital platforms, and emphasizes the crucial role of digital communication in the inheritance and promotion of intangible cultural heritage.

The purpose of this work is to explore methods of visual communication based on generative artificial intelligence in the context of new media. This work proposes an image automatic generation and recognition model that integrates the Conditional Generative Adversarial Network (CGAN) with the Transformer algorithm. The generator component of the model takes noise vectors and conditional variables as inputs. Subsequently, a Transformer module is incorporated, where the multi-head self-attention mechanism enables the model to establish complex relationships among different data points. This is further refined through linear transformations and activation functions to enhance feature representations. Ultimately, the self-attention mechanism captures the long-range dependencies within images, facilitating the generation of high-quality images that meet specific conditions. The model’s performance is assessed, and the findings show that the accuracy of the proposed model reaches 95.69%, exceeding the baseline algorithm Generative Adversarial Network by more than 4%. Additionally, the Peak Signal-to-Noise Ratio of the model’s algorithm is 33dB, and the Structural Similarity Index is 0.83, indicating higher image generation quality and recognition accuracy. Therefore, the model proposed achieves high recognition and prediction accuracy of generated images, and higher image quality, promising significant application value in visual communication in the new media era.

In order to solve the problems of inefficient allocation of teaching resources and inaccurate recommendation of learning paths in higher education, this paper proposes a smart education optimization model (SEOM) by combining the improved random forest algorithm (RFA) based on adaptive enhancement mechanism and the Graph Neural Network (GNN) algorithm. The public data and information such as the national higher education intelligent education platform are collected, and SEOM is trained and verified. The results show that SEOM has high accuracy and generalization ability in three different teaching scenes: online mixed teaching, personalized teaching and project-based teaching. The Root Mean Square Error (RMSE) value in cross-validation is between 0.2 and 0.5, and the Mean Absolute Error (MAE) value is between 0.1 and 0.5. SEOM shows strong stability when dealing with multidimensional educational resources and complex teaching modes. The accuracy rate remains at 85-97%, indicating its reliability in personalized learning path recommendation. Further analysis shows that the chi-square freedom ratio is between 1.0 and 2.5, the fitting index and the adjusted fitting index are both above 0.85, and the comparative fitting index is close to 0.95, which shows that SEOM has high accuracy and rationality in capturing the dependence of knowledge points in different teaching modes. The Root Mean Square Residual (RMR) and Root Mean Square Error of Approximation (RMSEA) are both below 0.05, which indicates that SEOM has small residual and strong scene adaptability. In addition, in the abnormal network environment, the resource allocation efficiency of SEOM is above 60%, and the Shapley value is between 0.1 and 0.4, which shows that SEOM can adapt to the change of network environment and the resource allocation effect is still obvious. Generally speaking, SEOM can optimize the allocation of educational resources and recommend learning paths in a complex environment, and effectively improve the intelligence and efficiency of teaching decision-making, especially for university administrators and educational technology developers.

This paper intends to solve the limitations of the existing methods to deal with the comments of tourist attractions. With the technical support of Artificial Intelligence (AI), an online comment method of tourist attractions based on text mining model and attention mechanism is proposed. In the process of text mining, the attention mechanism is used to calculate the contribution of each topic to text representation on the topic layer of Latent Dirichlet Allocation (LDA). The Bidirectional Recurrent Neural Network (BiGRU) can effectively capture the temporal relationship and semantic dependence in the text through its powerful sequence modeling ability, thus achieving a more accurate classification of emotional tendencies. In order to verify the performance of the proposed ATT-LDA- Bigelow model, online comments about tourist attractions are collected from Ctrip.com, and users’ emotional tendencies towards different scenic spots are analyzed. The results show that this model has the best emotion classification effect in online comments of scenic spots, with the accuracy and F1 value reaching 93.85% and 93.68% respectively, which is superior to other emotion classification models. The proposed method not only improves the accuracy of sentiment analysis, but also provides strong support for the optimization of tourism recommendation system and provides more comprehensive, objective and accurate tourism information for scenic spot managers and tourism enterprises. This achievement is expected to bring new enlightenment and breakthrough to the research and practice in related fields.

This work examines the application of Generative Artificial Intelligence (GAI) technology in animation teaching, focusing on its role in enhancing teaching quality and learning efficiency through innovative instructional strategies. Compared to traditional animation teaching methods, GAI technology introduces a novel pedagogical paradigm characterized by adaptive personalized learning pathways, intelligent teaching resource optimization, and immersive interactive learning models. A mixed-methods research approach is adopted, integrating quantitative analysis (experimental data and questionnaire surveys) and qualitative analysis (behavioral observations) to systematically assess the educational effectiveness of GAI technology. The experiment, conducted over 12 weeks, involved 120 students divided into an experimental group and a control group. Data sources included pre- and post-test evaluations, learning feedback surveys, and classroom behavior analysis. The results indicate that, compared to conventional teaching methods, GAI technology significantly enhances learning outcomes, knowledge application abilities, learning motivation, and student satisfaction. The adaptive personalized learning pathway dynamically adjusts content based on students’ progress, improving their mastery of foundational knowledge and skill transferability. Intelligent teaching resources automatically generate high-quality animation examples and provide dynamic feedback mechanisms, fostering creative expression and practical efficiency. The immersive interactive learning model effectively increases classroom engagement, teamwork skills, and problem-solving abilities. These findings demonstrate that GAI technology has the potential to transform animation teaching by optimizing the learning experience and advancing intelligent teaching methodologies. Beyond offering personalized learning solutions, GAI technology plays a crucial role in cultivating students’ creativity, critical thinking, and autonomous learning abilities. This work provides theoretical support and practical guidance for the digital transformation of animation teaching while underscoring the broader applicability of GAI technology in the education sector, offering new directions for the future development of intelligent education.

This work aims to enhance the accuracy and efficiency of corporate strategic decision-making, particularly in rapidly changing and highly competitive market environments. Traditional strategic decision-making methods rely on managers’ experiential judgment and exhibit limitations when handling complex data and high-frequency market fluctuations. To address this issue, this work proposes a hybrid optimization model combining transformer models and reinforcement learning algorithms, designed to optimize corporate strategic decision-making processes and improve competitiveness. First, relevant studies on strategic decision-making and corporate competitiveness are reviewed, clarifying the potential and advantages of artificial intelligence (AI) in decision support. Second, the hybrid model is developed and trained through steps including data collection and preprocessing, algorithm selection and model construction, as well as model training and validation. Finally, real-world data are applied to evaluate model performance across indicators such as training time, convergence speed, and prediction effectiveness. The results demonstrate that the hybrid model successfully converges within 150 iterations and exhibits substantial advantages over traditional algorithms, particularly in prediction accuracy for market share (92%), profit growth rate (91%), and customer satisfaction (89%). Implementing the model leads to notable improvements in corporate market position, brand influence, and technological innovation capabilities. The work shows that the hybrid model enhances the scientific rigor and accuracy of decision-making. Meanwhile, it strengthens corporate competitiveness and market responsiveness, highlighting the substantial potential of AI technologies in strategic management. This work provides enterprises with an efficient and reliable decision-support tool, facilitating the maintenance of competitive advantages in complex and dynamic market environments.