The sharable e-learning platform can be presented as a useful learning environment for students on the cloud computing infrastructure. Virtual classrooms are momentarily taking the place of conventional ones, which means that e-learning is becoming more popular. There are currently no strategies for estimating how much cloud resources will be used. Because of this, students can access learning objects without deciding to follow a different learning management system (LMS). The proposed deep learning-based e-learning platform (DL-E-LP) can enable separate LMS embedded in multiple e-learning standards to share the learning objects. Using a smart learning system, teachers can keep track of their students' progress more easily. The convolutional neural network has been used to develop face recognition and monitor students' knowledge learning level in deep learning. The use of modern technologies and smart classrooms makes learning easier for all students. The proposed paradigm is both efficient and productive through experimentation.

The distance teaching of digital media network course under the network environment can improve the pertinence of teaching and the sharing of teaching resources. On this basis, a distance teaching system of digital media online course based on improved genetic algorithm and speech recognition technology is designed. The online course teaching system consists of network communication module, data acquisition module, bus transmission module and application loading module. The module connection and function control of online course teaching system are realized under VME bus architecture. Based on improved genetic algorithm and speech recognition technology, using eclipse as the development environment, the data storage layer, user analysis layer and log mining layer of online course teaching are constructed. The control and structure layout of online course teaching terminal are realized on the user interface, and the system optimization design is completed. The system test results show that the bus data transmission performance of the system for online course teaching is good, and it can effectively meet the personalized needs.