Research on Interactive Interface Adaptive Design Model Based on Dynamic Cognitive Load Evaluation

With the increasing complexity of human-computer interaction systems, the cognitive load caused by interface information overload has become a key bottleneck affecting user experience and operational efficiency. Therefore, this paper proposes an interactive interface adaptive design model based on dynamic cognitive load evaluation and constructs a closed-loop optimization framework of “perception-evaluation-decision-execution”. Therefore, this paper proposes an interactive interface adaptive design model based on the dynamic evaluation of cognitive load, and constructs a closed-loop optimization framework of “perception evaluation decision execution”. First, a dynamic multimodal cognitive load assessment model is designed, which integrates behavioral, eye-movement, and physiological signals via a cross-modal attention mechanism, combined with time series modeling and uncertainty estimation, to achieve continuous and accurate perception of cognitive load. The root mean square error of prediction is 0.592. Second, a cognitive-driven interface adaptive decision-making framework is constructed, which uses the results of cognitive load assessment and task context as the basis for decision-making. On this basis, a cognitive-constrained reinforcement learning optimization algorithm is proposed. By introducing an upper-limit constraint on cognitive load and a strategy clipping mechanism, interaction efficiency is guaranteed and decision stability is improved. Experimental results show that the proposed method reduces task completion time by 22.7%, increases user satisfaction by 41.9%, decreases cognitive load by 25.0%, and keeps total response delay within 81 milliseconds. This study provides a systematic solution for the construction of intelligent adaptive interface with both evaluation accuracy and decision stability.