随着城市空中交通（Urban Air Mobility， UAM）商业化运输的兴起，简化飞行操纵（SVO）的概念被引入航空器设计，旨在简化操作流程以适应未来运输需求。然而，基于SVO设计的电动垂直起降飞行器（eVTOL）是否满足适航性条件，以及其操纵界面是否符合人机交互的友好性标准，目前尚存在不确定性。为了解决这一问题，对基于简化操纵设计的eVTOL进行了任务科目基元（MTE）的实验，以评估其操纵品质。实验共招募了20名被试人员，通过库珀-哈珀量表收集了他们对eVTOL操纵品质的主观评分，并进行了半结构化访谈，同时记录了他们的眼动数据和肌电数据。本研究在结合主观评估和可解释的手工特征的基础上，提出了基于格拉米角场法和卷积神经网络的操纵品质评估模型。研究结果表明，不良的操纵界面设计显著影响了参与者的肌电信号和眼动信号。基于此结论设计的卷积神经网络模型能够以93.67%的准确率评估eVTOL的操纵品质。研究不仅为航空器操纵品质的提供了更为客观的新视角，而且为未来简化操纵eVTOL的设计提供了重要的改进依据。

With the advent of commercial transportation in Urban Air Mobility （UAM）， the concept of Simplified Vehicle Operations （SVO） has been integrated into aircraft design， aiming to streamline operational procedures to meet future transport demands. However， there is uncertainty regarding whether electric Vertical Take-Off and Landing （eVTOL） aircraft， which are designed based on SVO， meet airworthiness criteria and whether their control interfaces adhere to ergonomic standards for user-friendliness. To address this issue， an experiment on Mission Task Elements （MTE） was conducted to assess the handling qualities of SVO-based eVTOL aircraft. 20 participants were recruited for the experiment， during which their subjective ratings of handling qualities using Cooper-Harper Rating Scale， qualitative comments through semi-structured interviews， and electromyography （EMG） data and eye-tracking data were recorded. Additionally， a handling qualities assessment model based on Gramian Angular Field （GAF） and 2D-Convolutional Neural Networks （2D-CNN） was proposed. The results indicate that poor control interface design significantly affected the participants‍’ EMG and eye-tracking signals. Benefiting from the spatio-temporal information provided by GAF images， the proposed 2D-CNN achieved an accuracy of 93.6% in predicting eVTOL handing qualities levels. This study provides a new perspective for the objective assessment of eVTOL handling qualities and offers significant guidance for the future design of SVO.