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As China's economic level and industrial volume continue to develop and expand, the traditional methods of business management and resource scheduling demonstrate significant limitations and misalignments with China's daily economic activities. Traditional industrial and commercial management resource scheduling is highly reliant on manual labor. When confronted with the requirements of large-scale project management, the pure manual mode cannot track the real-time progress of the project flexibly, effectively, and in a timely manner, resulting in the inability to complete the corresponding resource-scheduling work efficiently, which will affect the overall operation progress of the project. Deep neural networks have a high-application potential for solving extremely complex and highly nonlinear optimization problems. Artificial intelligence and deep learning research have advanced at a rapid pace over the past few decades, thanks to the efforts of numerous researchers. Combined with GPU technology, the deep learning framework can provide an extremely complex optimization problem with a practical and feasible optimization scheme and corresponding solution path in a very short amount of time. Therefore, this paper explores the potential application of deep learning technology to industrial and commercial resource-scheduling management. By analyzing the benefits of deep learning technology and the bottleneck issues of existing industrial and commercial resource scheduling, a real-time optimized industrial and commercial resource-scheduling model based on deep learning technology is developed. The model is evaluated using the respective data set. The test results demonstrate that the resource-scheduling model proposed in this paper has strong real-time and high-operation efficiency and can assist engineers in completing the corresponding resource-scheduling tasks. [ABSTRACT FROM AUTHOR]

In this paper, we propose a computable method to evaluate the aesthetic value of fusion images of folk martial arts and dance based on human visual and aesthetic habits, extract features and use them as evaluation indexes from three aspects: technical features, perceptual features, and social features, establish an aesthetic evaluation model by fusing each index, and determine the influence factors of each index by using online research. The aesthetic evaluation of fusion images of folk martial arts and dance is carried out automatically. The results of the experimental tests on the evaluation dataset of the aesthetic quality of fusion images of folk martial arts and dance at the Communication University of China show that the accuracy of the proposed deep learning algorithm-based method for analyzing the aesthetic index of fusion of folk martial arts and dance can reach 98.08% with certain validity, and the evaluation results of each index are clear and intuitive, which can play a guiding role in improving the aesthetic quality of fusion of folk martial arts and dance from various angles. It can be used as a guide to improving the aesthetics of the fusion of folk martial arts and dance from various perspectives. By integrating the model into the mobile application, it is possible to evaluate and score the aesthetics of multiple portrait photos uploaded by users and select photos to be kept or deleted based on the scoring results, thus simplifying user operations. [ABSTRACT FROM AUTHOR]