Simulation of association rule mining based on sensor networks in Chinese language learning recommendation system for college students

Abstract

Based on the increasing learning needs of students that cannot be met by traditional education in schools, the use of online learning platforms has become a way for many college students to learn Chinese, thereby improving their academic performance and literary literacy. However, it is difficult to guarantee the service quality of online platforms for Chinese learning at present, so this paper proposes an association rule mining algorithm to strengthen Chinese online learning. We using user service quality as a constraint to improve spectrum utilization and energy efficiency, and defining the user state space, borrowing the obtained resource allocation optimization function to reward a small portion of users' communication costs, ultimately obtaining user state space information and one-dimensional state space data. This algorithm performs grouping operations on the data estimated by a large amount of computation, and divides the data into balanced categories to reduce the amount of input data in the network. The performance test results show that this paper has made a great breakthrough in the personalization of Chinese learning, and has outstanding performance in the processing and classification of Big data. There are also some solutions to the problem of too much existing data. In a period of use experience analysis report, we found that the online platform for Chinese learning can give consideration to students' personalized needs and experience.

Keywords

• Reinforcement learning
• Association rules
• Online learning
• Resource recommendation