In the big data era, digital marketing data are strategic information resources, yet mid-sized firms face multi-channel resource allocation dilemmas. Working from an information resource management perspective, this exploratory teaching case develops a fuzzy evaluation-based decision support framework and validates it via Company A, a mid-sized Chinese fashion brand. It details the firm's actual managerial predicaments, full model implementation, and empirical results—reallocating marketing resources to high-engagement digital channels improved digital marketing ROI by 18% after implementation. The case provides a complete narrative, including transparent methodological steps and comprehensive pedagogical components (learning objectives and discussion questions). The findings offer transferable insights for information technology-enabled management and serve as a valuable teaching reference for information system and marketing courses.

This study aims to explore a scientific and systematic design method for product packaging by integrating perceptual engineering, interactive genetic algorithms, and traditional cultural elements. A case study on daily necessities packaging was conducted, in which the semantic difference method was used to collect perceptual evaluations from 20 subjects. The proposed model was verified through 800 iterations of a genetic algorithm. The results show that the proposed method significantly improves the gene matching rate of packaging designs compared to traditional approaches. After optimization, the interactive genetic algorithm achieved a satisfaction score of approximately 9 points out of 10. The integration of enterprise information systems reduced material procurement approval cycles by 50% and enhanced the accuracy of market acceptance prediction for new designs. This study provides a novel paradigm for modern packaging design, demonstrating that the integration of perceptual engineering, genetic algorithms, and enterprise information systems can effectively balance visual appeal and market feasibility.

This study explores the use of artificial intelligence technology to build an adaptive learning system to promote the innovation of college English teaching. By integrating natural language processing, deep learning, and recommendation algorithms, a multidimensional adaptive learning framework for college English is constructed that provides personalized support in various skills. By designing comparative teaching experiments and collecting learning behavior data, the effectiveness of the model is systematically evaluated. Experiments show that the system can significantly improve the utilization rate of teaching resources, learning efficiency, and interactive experience. Students who use the system have made remarkable progress in vocabulary mastery, writing ability, and oral expression, and their satisfaction is high. Students with different learning styles and levels can get adaptive learning paths and real-time feedback. The adaptive learning system driven by artificial intelligence shows good applicability and popularization potential in college English teaching.

In the digital transformation era, the application of deep learning in image style conversion has opened new avenues for creative expression and business innovation. This article explores advanced deep learning techniques to achieve efficient and accurate image style conversion in different image datasets. The method has been validated through comprehensive experiments on the Microsoft Common Objects in Context dataset, and compared to existing methods, this article's method demonstrates outstanding performance metrics including peak signal-to-noise ratio, structural similarity, and user satisfaction. In addition, the impact of different hardware configurations on processing efficiency was analyzed, providing insights into optimizing computing resources for real-time applications. The results of this case study not only contribute to the theoretical advancement of image processing but also provide practical solutions for the visual content creation industry.

In the era of big data, China's national folk music dissemination faces key challenges: information asymmetry, weak content diffusion, and excessive commercialization. This study explored the music's transmission mechanisms via a discrete dynamic analysis framework, drawing on empirical data (12 provinces, 200,000+ user profiles) to highlight the traditional methods' limitations and new media's transformative potential. Integrating big-data analytics, cloud computing, and artificial intelligence, the study proposed strategies to enhance accessibility, cultural relevance, and sustainability—strengthening cultural protection, targeted audience engagement, and new media expansion to bridge regional and technological gaps. Findings confirmed big data's efficacy (14.9 times broader reach, 31.3% higher cultural retention versus traditional media), emphasizing balancing modernization with preservation for folk music's survival and global recognition.

This paper presents a method for the digital reconstruction and visual effect optimization of movie scenes based on computer-aided design–multimedia synergy, with the aim of addressing the inefficiencies and low accuracy of traditional reconstruction methods. The method integrates computer-aided design-based parametric modeling for high-precision geometric reconstruction and combines multimedia's rendering and real-time interaction capabilities to achieve realistic visuals via ray tracing and particle systems. To further improve accuracy, the author used a multisource data fusion strategy that integrates laser scanning and photogrammetry. In addition, an adaptive cross-platform rendering scheme was designed to optimize display effects on PCs, virtual reality devices, and mobile terminals. Experiments indicated that compared with Blender's native workflow, this method shortens the PC rendering time by 37.8% and the mobile modeling cycle by 39.8%, with a light-shadow error of 8%. Overall, this work provides technical support for efficient and high-quality digital film production.

Although multimedia technology has been widely used in language teaching, there are many visual quality problems in teaching resources, such as blurred images, compression distortion, and unbalanced brightness contrast, which seriously affect students' cognitive efficiency and learning experience. However, the existing practice lacks a systematic technical quality control mechanism. The purpose of this study is to design and verify a closed-loop optimization framework driven by information technology, so as to solve the core problem of unstable quality of multimedia resources in middle school English teaching, and explore the intelligent upgrading path of teaching media from available to high quality through algorithms and data. A one-year educational technology intervention case was put forward and implemented. The experimental results show that the students' grades and satisfaction with the optimized resources are significantly improved. This case provides a replicable and quantifiable optimization paradigm for technology empowerment teaching in the field of educational technology.

As cultural tourism continues to evolve, enhancing the cultural depth and immersive experience of tourist products through Computer-Aided Design (CAD) and multimedia technologies has become a key issue in design and industrial innovation. Focusing on tourist product design, this study systematically explores CAD's strengths in spatial modeling, structural optimization, and form generation while examining how multimedia interaction and digital content enhance cultural expression and immersive atmospheres. Through a representative case, it analyzes challenges in traditional design, such as shallow user experience and weak cultural delivery, and highlights the necessity of deep CAD-multimedia integration. A new development framework combining multi-level CAD modeling with dynamic multimedia interaction is proposed, with the aim of improving product uniqueness and emotional engagement. The study clarifies the innovation mechanism and validates the approach, offering both theoretical insights and practical guidance for tourism design and industry transformation.

Against the backdrop of intelligent logistics growth and talent shortages, this study aims to fill gaps in the integration of the internet of things, artificial intelligence, and big data with industry-education collaboration. Adopting a single-case research design, data were collected over 12 months (September 2023–August 2024) via semi-structured interviews, operational records, graduate surveys, and document analysis. Reliability was ensured through triangulation and inter-rater validation. Technology integration reduced cold-chain goods loss by 20%, increased automated sorting speed by 25%, and cut overall logistics costs by 15%. At the same time, industry-education integration boosted graduates' employment rate to 85%, with enterprise satisfaction reaching 4.2 out of 5, outperforming peer programs. Minor issues such as insufficient enterprise investment were identified and addressed. This study enriches the theoretical understanding on intelligent logistics technology-education integration and provides a replicable framework for training-based construction, alleviating industry talent shortages.

The monitoring of the survey activities plays an important role for the governments, which refers to the civil construction, economic development and even the information security. The detection and recognition of the survey behavior is basis for monitoring. An approach based on multi-layer fully connected neural network for RTK trajectory is presented in this paper. At first the feature space is constructed. The back propagation neural network is used to train the sample sets as 5 typical survey behaviors as points, lines, planar, account sharing and invalid surveys. The simulation tests of BP-NN parameters as topology, learning rate and dropout ratio are conducted. With real datasets from HNCORS services, the approach is validated. The experiments results show that, the training and testing ORA are 84.7% and 82.8%. It can recognize the account sharing, points, lines, planar and invalid type survey behavior. Especially for invalid and account sharing types, the TRA reach 93.6% and 88.5%. Compared to the KNN and SVM algorithm, the testing ORA is improved by 15.3% and 14.8%.