Evaluation modeling of the main flight display interface in the cockpit of the Boeing 747 airplane based on fluency theory

Based on the continuous development of artificial intelligence (AI) in interface recognition technology, the purpose of this article is to build a PFD interface fluency classification model, providing fluency level evaluation for future interface optimization design. The article introduces the influencing factors of fluency theory on the interface of the Boeing 747 PFD (Main Flight Display), and explores the effects of low, medium, and high fluency levels on participants browsing PFD interface information. By optimizing three interface factors, including font size, color contrast, and graphic outline, different levels of smoothness were constructed. Experiments were designed to collect participants' reaction time, accuracy, blink frequency, and subjective evaluations for tasks with different fluency levels. Finally, an interface fluency recognition and classification model was established using the Fisher discriminant method, and its effectiveness was verified. This model replaces the traditional single subjective emotional evaluation and forms an intuitive and quantitative interface design evaluation method. This method provides designers with a new evaluation approach, breaking free from the limitations of subjective evaluation.