Dynamic Optimization of Human-Computer Interaction Interfaces Using Graph Convolutional Networks and Q-Learning

Abstract

With the rapid development of artificial intelligence technology, intelligent human-computer interaction systems have gradually become an important tool for improving user experience and work efficiency. Traditional human-computer interaction interfaces usually rely on static rules and fixed layouts, which are difficult to adapt to the changing needs of users and complex operating environments. To solve this problem, this paper proposes a dynamic interaction interface optimization method based on graph convolutional networks (GCNs) and Q-learning. This method combines the powerful feature extraction capabilities of graph convolutional networks with the adaptive optimization characteristics of Q-learning and can dynamically adjust the layout and response strategy of the interface to meet the needs of different users and environments. First, GCN is used to extract graph structure features from user interaction data to capture the complex relationship between interface elements; then, combined with the Q-learning algorithm, the response strategy of the interface is optimized through reinforcement learning to improve the flexibility and real-time adaptability of the system. Experimental results show that the optimization method based on GCN and Q-learning is superior to traditional static layout methods and deep learning feature extraction methods in terms of user satisfaction, response time, and operating efficiency, verifying the effectiveness of this method in dynamically optimizing interaction interfaces. This study provides a new idea for the intelligent optimization of human-computer interaction interfaces and provides technical support for future applications in smart devices and virtual reality.