Design Analysis of Human-Computer Interaction and Information Communication in Artificial Intelligence Environments

Introduction

In the field of human-computer interaction (HCI), how to improve system performance and enhance user experience has always been one of the core research issues. With the rapid development of technologies such as big data (BD), wireless sensor networks (WSN), and the Internet of Things (IoT), traditional HCI interface design methods are facing many challenges, such as high cost, low interaction efficiency, and system response speed. Therefore, exploring more efficient and intelligent optimization techniques has become the key to improving HCI performance. This study proposes an innovative optimization scheme for evaluating and developing future HCI interfaces by introducing augmented scalar computation algorithm (ASCA) and genetic algorithm (GA). This method significantly improves the accuracy, response speed, and operational efficiency of the system by exploring the interaction between two communication modes. Research has shown that the proposed optimization method exhibits significant advantages in multiple key indicators compared to existing technological solutions. This study provides valuable insights for HCI design and verifies the feasibility and superiority of the proposed optimization scheme through actual data and experimental results, laying a solid foundation for future related research.