Abstract
This study focuses on the application of user interface adaptive algorithm in multimodal interaction in intelligent connected vehicles. With the rapid development of intelligent vehicle networking technology, the user interface needs to dynamically adapt to different user needs and environmental changes in complex interaction scenarios. This paper proposes a user interface adaptive model that combines Markov chain algorithm and Double Deep Q Network (DDQN) algorithm to achieve intelligent decision-making and adaptive optimization in multimodal interaction. The model analyzes the user's historical interaction behavior, uses Markov chain to predict the user's operation sequence, and optimizes the interaction mode in different scenarios through DDQN algorithm, and finally realizes the adaptive adjustment of the user interface. Simulation results show that the adaptive algorithm can effectively improve the response speed and accuracy of the user interface in complex environments, while greatly reducing the complexity of user operations. Accurate data analysis further proves that the model shows high flexibility and robustness in multimodal interaction, providing new ideas and technical support for the optimization of user experience of intelligent connected vehicles.