Financial digital management is a new type of financial management method. Through information technology, the financial management process has been digitized, and with the help of technical means such as data analysis and artificial intelligence, financial management automation has been achieved. Traditional financial management methods often require a large amount of manual intervention and processing, which is prone to problems such as cumbersome data processing, time-consuming and labor-intensive, and prone to errors. With the development of computer technology and network technology, digital management has become a new trend in financial management. This article analyzed the application of blockchain and cloud computing technology in financial digital management, and selected 12 enterprises as the research objects. The traditional financial management model and the financial digital management model of blockchain and cloud computing technology were respectively adopted to compare the differences in financial process efficiency, data accuracy, labor cost savings, digital management, and financial risk management between the two models. The experimental results of this article indicated that under the financial digital management mode using blockchain and cloud computing technology, the processing time of the revenue and expenditure process was 4.45 hours in terms of financial process efficiency. In terms of data accuracy, the accuracy rate of accounting was 99.7%. In terms of labor cost savings, the labor cost was 1.505 million yuan/year. In digital management, the data processing efficiency score was 92. In financial risk management, the accuracy score of risk assessment and prediction was 93, which was better than traditional financial management models. The adoption of blockchain and cloud computing technology in financial digital management can significantly improve multiple key indicators such as financial management efficiency, data accuracy, and security. This model has important value and significance for enterprises.

The popularity of the Internet and mobile smart terminals has changed many forms of learning, and the mobile learning model was born in this environment. As a new learning mode, mobile learning has brought certain development opportunities for college English writing teaching. In the current educational environment, many students hold various mobile devices, which also motivates them to have a strong willingness to learn on mobile. It can be said that the application of mobile learning to English writing is quite suitable. At present, the application of mobile learning in college English writing is not mature enough, and there are often a series of problems such as shortage of resources and network freezes, which also reduces students’ enthusiasm for learning. In order to further improve the fluency and maturity of the mobile learning mode, this paper has combined the wireless network to study the new mobile learning mode of college English writing. By building a mobile learning framework based on wireless network, innovating mobile learning writing content and computing learning resource categories, a new mobile learning mode of college English writing has been finally formed. The experimental results have shown that the new model has mobilized students’ enthusiasm for learning and further improved the writing efficiency. Compared with the old model, the efficiency has increased by 6.73%.

Traditional power load forecasting (PLF) usually uses statistical models or time series analysis methods, but they often only consider historical load data and ignore the impact of meteorological, temperature, humidity and other factors on load, resulting in inaccurate load forecasting. Moreover, traditional methods have limited real-time performance in power load data transmission and cannot respond to changing load demands in a timely manner, which limits the real-time and accuracy of PLF. Wireless networks (WN) and intelligent sensing technology (IST) were used to obtain real-time charge data, and these data were intelligently analyzed to improve prediction performance. WN and IST were used to improve the transmission efficiency and prediction accuracy of PLF. This article studied the transmission delay and integration delay of power load data in WN, and conducted experimental tests on the root mean square error (RMSE) of CER Electricity Data, REFIT Power Data, and Umass Smart Data Set datasets using an intelligent sensing algorithm based on sensors to study their predictive effect on power load. As the number of users continues to increase, the transmission delay and integration delay of power load data were also increasing. During the process of increasing the number of users from 0 to 500, the transmission delay increased from 389ms to 735ms; the integration delay increased from 568ms to 1086ms. The power load prediction algorithm based on intelligent perception technology had average prediction RMSEs of 0.2885, 0.2716, and 0.2618 for CER Electricity Data, REFIT Power Data, and Umass Smart Data Set datasets, respectively. In WN, the transmission delay and integration delay of power load data are relatively small, and with the increase of the number of users, the impact of this delay is relatively small, which can have the effect of supporting the transmission and integration of power data for a large number of users. The power load prediction algorithm based on intelligent perception technology has good prediction results for different datasets and can accurately predict power loads.

This research presents an innovative machine learning framework for predicting library space utilization patterns through the integration of multi-modal deep learning architectures and ensemble methodologies. The proposed system combines Long Short-Term Memory (LSTM) networks with attention mechanisms and sophisticated feature engineering techniques to achieve superior prediction accuracy while maintaining computational efficiency. The methodology encompasses three primary contributions: (1) development of a comprehensive feature extraction pipeline incorporating spatial, temporal, and environmental data streams; (2) implementation of a novel LSTM-Attention hybrid architecture with adaptive learning rate optimization; and (3) integration of ensemble learning techniques for robust prediction performance. The framework demonstrates significant improvements over existing approaches, achieving 96.8% prediction accuracy across diverse operational scenarios. Experimental validation, conducted using an extensive dataset comprising 2.1M samples collected over 33 months from multiple library facilities, demonstrates the framework’s effectiveness. The proposed model achieves a Mean Absolute Error (MAE) of 0.142 and Root Mean Square Error (RMSE) of 0.186, representing a 39.8% reduction in prediction error compared to baseline approaches. The system’s computational efficiency is evidenced by an average processing time of 45.3ms per prediction, with a memory footprint of 512MB. The research contributes to the field of intelligent library management systems by establishing a theoretically grounded and practically implementable solution for space utilization prediction. The framework’s superior performance in capturing complex spatial-temporal patterns, combined with its computational efficiency, makes it suitable for real-time applications in resource-constrained environments. These advances provide a foundation for enhanced space management strategies in modern library systems.

Amidst the digital economy and ESG policy frameworks, digital transformation emerges as the prime strategy for high-tech companies to enhance their corporate performance. The research investigates the impact of high-tech organizations’ digital transformation on their performance, utilizing data from A-share listed tech firms in Shanghai and Shenzhen spanning 2018 to 2022.The research indicates that digital transformation enhances the performance of high-tech firms in the context of ESG. The modulating mechanism shows that executive compensation will weaken the impact of digital transformation on enterprise performance. The intermediary mechanism demonstrates that internal control and cost effect contribute to the mediating influence on the relationship between enterprise performance and digital transformation. Each of them has successfully cleared multiple tests for robustness. At the same time, there is a certain heterogeneity in the influence of high-tech enterprises on firm performance, and the improvement effect on firm performance is significant in the east and the growth and maturity period. The research presents new empirical evidence and acts as a benchmark for understanding how digital transformation affects high-tech companies’ performance.

Purpose – This study aimed to explore the internal structure of sustainable employability of liberal arts college students in China and develop a comprehensive scale to facilitate research on this topic and establish a theoretical framework for cultivating sustainable employability of liberal arts college students in China. Design/methodology/approach – Through theoretical derivation and open questionnaire and the Delphi method, the main dimensions of sustainable employability of liberal arts college students are explored. The components elments of each dimension are explored through a text analysis of 189 job advertisements. Through 392 questionnaires and statistical analysis techniques, a scale is developed for measuring the sustainable employability of liberal arts college students. Findings – This study found three dimensions characterizing the sustainable employability of liberal arts students in China: attribute characteristics, general ability of employment, and innovation-driven ability. Additionally, the attribute characteristics encompassed five attribute elements, the general employment ability included six, and the innovation-driven ability included four. This study also developed a 34-item scale for measuring the sustainable employability of liberal arts students that demonstrated good reliability and validity. Originality/value – This study was among the first to investigate the internal structure of sustainable employability of liberal arts students in China.

This study introduces a new methodology for the configuration and optimization of algorithm-driven strategies in the digital economy. It puts forward a hybrid optimization algorithm for the efficient handling of complex resource allocation problems. The proposed approach combines adaptive learning mechanisms with traditional optimization methods, showing significant improvement in convergence speed, solution accuracy, and stability of the system. Through extensive experimental validation conducted on a range of benchmark functions and real-world contexts, this algorithm proves to be outstanding at a 48.7% reduction in convergence time, as well as a solution quality enhancement by 66.4% compared with the traditional methods. Robustness analysis confirms consistent effectiveness under all diverse noise conditions and retains high success rates, even in demanding environments. This result greatly contributes to advancing algorithmic optimization approaches for digital economic systems and paves the way toward concrete applicative implementations.

With the rapid development of artificial intelligence technology, the education sector is undergoing unprecedented changes. Personalized learning has become a key method to enhance teaching quality and learning outcomes. This paper aims to explore the application of artificial intelligence technology in personalized learning resource recommendation for students, by constructing user profiles, multidimensional models, and personalized recommendation algorithms, in order to provide precise learning resource recommendations for students. This paper proposes a personalized learning resource recommendation algorithm based on a one-dimensional convolutional neural network (1D-CNN). The algorithm first extracts local features of the sequence through convolutional operations, then uses pooling operations to extract long-term features of the sequence, and combines the two features through weighted addition to obtain the user feature information, which allows for the comprehensive extraction of both local and long-term features. Subsequently, the user feature information is multiplied by the linearly transformed sequence information to introduce temporal information. Additionally, student learning records, class performance, and incorrect question records are collected and integrated as user feature information. These user features are passed through a feedforward network to achieve nonlinear transformation and cross-dimensional interaction enhancement. Finally, the user feature vector and item feature vector are computed to obtain their relevance, which is then used for recommendations. Experimental evaluations validate the effectiveness and feasibility of the proposed method, with the aim of providing valuable insights for educational reform and development.

The rapid development of digital technology and artificial intelligence has made the improvement and optimization of intelligent warehousing and automated distribution systems important topics for research in modern logistics management. With this as the background, the current study uses a systematic approach to explore critical factors, innovative ways, and implementation strategies related to these factors and their role in improving the effectiveness of intelligent warehousing systems. The study adopts a mixed-methodological approach, establishing a comprehensive evaluation index system including operational efficiency, technical performance, and economic benefits, and simultaneously verifying the implementation of the system through empirical analysis. According to the findings, the intelligent warehousing system increased the efficiency of operations in relation to order processing time and had reduced it by 71.7%, and enhanced the accuracy of picking to 99.8%. The intelligent warehouse system by use of machine learning and meta-heuristic algorithms had greatly improved the efficiency in resources utilization and energy as storage utilization increased by 19.3% while energy consumption dropped by 31.4%. A cost-benefit analysis shows that, despite the significant up-front financial investment, the system achieved a 186% return on investment over three years. This research deepens the theoretical understanding of intelligent warehousing and, at the same time, provides optimization strategies applicable to industry practice. Future research directions should focus on exploring the applications of multi-agent digital twin technology and researching how intelligent warehousing systems contribute to supply chain resilience and sustainability.

Power metering system is directly related to the production and operation level and benefit of power supply enterprises, and even has a close relationship with the national economic development and people’s life. Numerous scholars have applied deep learning to the field of fault diagnosis. Accordingly, this paper proposes a fault diagnosis method for power metering system based on stacked autoencoder (SAE) algorithm. The deep learning data samples are formed by comprehensively collecting the historical operation data of the system and the feature data provided by the third-party manufacturers. And the fault diagnosis model is designed with the SAE algorithm, and the training and optimization fine-tuning of the algorithm model is realized by BP neural network. Finally, the model is trained using explicit test data samples, and the BP neural network can reach the set accuracy after 3804 training sessions with the output error. Compared to Elman neural network iterations are less and converge faster. Using the trained fault detection model of power metering system for fault diagnosis, the model can successfully classify the faults and achieve the expected diagnostic effect.