The rapid change of technology has driven the information industry to advance and develop, and the computer network technology has been greatly improved, which also promotes the artistic expression of visual communication to develop in the direction of diversification and enrichment, providing designers with more sufficient performance carriers in visual communication design. As an essential communication method and medium in modern art and design, multimedia technology assumes the role of information transmission and communication in the teaching process of visual communication design and is an important communication platform. At the same time, in order to meet the market demand for talents, visual communication professional teaching is also faced with the contemporary task of cultivating innovative talents. And multimedia technology is gradually being applied in the field of education and teaching, becoming one of the driving forces to promote the evolution of teaching methods and teaching modes. Multimedia technology, as a medium of visual symbolic communication with the times, can not only expand and extend the depth and breadth of visual communication professional teaching but also promote the improvement of teaching methods and modes. This paper proposes that, against the background of continuous technological innovation, it is very necessary to further integrate multimedia technology into the teaching of visual communication majors. This paper discusses and analyzes and studies the application of multimedia technology in the teaching of visual communication from the aspect of innovative development in the teaching of visual communication.

The density-based applied spatial clustering algorithm is an algorithm based on high-density interconnected regions, which discovers class clusters of arbitrary shapes in noisy data sets and is widely used. However, it suffers from slow computation speed due to large-scale disk I/O and clustering bias due to uneven density class clusters and poor parameter search ability. To address these problems, a parallel density clustering algorithm based on an improved fruit fly optimization algorithm and Spark memory iteration is proposed. The proposed algorithm first divides the data grid using an irregular dynamic density region partitioning strategy. Then, a hybrid fruit fly particle swarm algorithm based on a genetic optimization mechanism is proposed to achieve dynamic optimization seeking for parameters in local clustering to improve the clustering effect of local clustering. Finally, the local merging of samples in irregularly bounded grid cells under each partition is achieved by designing a custom clustering merging strategy. The experiments show that the improved algorithm is generally applicable to the clustering of different shape class clusters and larger scale data and has obvious improvement in accuracy and parallel efficiency.

The Holy Qur’an has recently gained recognition in the field of speech-processing research. It is the central book of Islam, from which Muslims derive their religious teachings. The Qur’an is the primary source and highest authority for all Islamic beliefs and legislation. It is also one of the most widely memorized and recited texts around the world. Listening to and reciting the Qur’an is one of the most important daily practices for Muslims. In this study, we propose a deep learning model using convolutional neural networks (CNNs) and a dataset consisting of seven well-known reciters. We utilize mel frequency cepstral coefficients (MFCCs) to extract and evaluate information from audio sources. We compare our proposed model to different deep learning and machine learning methodologies. Our proposed model outperformed the competing models with an accuracy of 99.66%, compared to the support vector machine’s accuracy of 99%.

The electricity marketing management information system realizes the whole process of management from accepting customers’ electricity application to electricity bill recovery and from business processing to decision support. The electricity marketing management information system is a modern management tool for electric electricity enterprises to go to the market, expand the market, increase supply, expand sales, and provide customers with high-quality services. This paper has completed the following work: (1) described the development of domestic and foreign electricity marketing management information system and provided a theoretical basis for the design scheme proposed later. (2) The construction of the marketing system should focus on eight key points, establish a service-oriented architecture, plan the hardware platform in combination with the principles of advanced and practicality, and at the same time, apply multisensors to the marketing technology support system to improve the efficiency of data interaction. (3) According to the characteristics of the application software of the electricity marketing management information system, the software that runs between the components of the distributed, enterprise-wide C/S application or within the components is adopted. The performance of the software and hardware of the system is tested experimentally, and the results show that the electricity marketing information system has good performance.

With the surge in the amount of data in the internal and external environment, the collection, analysis, processing, and storage of the increasing data sources and data volume, as well as the problems of big data management, are the current situation and dilemmas of data management faced by enterprises today. Cognitive science and big data technology can provide good auxiliary support for enterprise management decision-making. This study takes the business information management of cold chain logistics enterprises as an example, aiming at the characteristics of business intelligence data in real applications, based on cognitive science and big data technology, from low-cost and high-performance storage, security management, and big data analysis. This paper is mainly through the research of big data processing theory and key technologies. Based on analyzing the logistics industry’s data access rules and characteristics, this study proposes a hot data prediction model for multilayer hybrid storage systems. It is verified that the prediction model has good accuracy, robustness, and universality. For the application scenario of multitenant distributed data access, a data transparent security management model is proposed. Simulation experiments show that this method can realize data security management when the performance loss is controlled within an acceptable range. Based on the real-time computing technology of massive data, the label optimization scheme of collaborative filtering and reinforcement learning is used to realize the logistics distribution recommendation model and to solve the accuracy and real-time problems of logistics service distribution analysis.