在线学习由于其智能化和个性化愈发成为人们青睐的主流学习方式，然“情知分离”现象的存在严重阻碍了在线教学深层发展，如何即时、精确感知学习情感进而为改善学习绩效提供参考便亟待研究。文章构建多模态数据融合的在线学习情感计算模型，采集被试面部表情、语音和文本数据，借助情感识别模型获取各模态情感识别结果。在此基础之上，通过基于决策级融合的方式实现多模态在线学习情感计算，并确定最优情感计算模型。研究发现，最优情感计算模型的平均识别精度较单模态情感识别提高了14.51%，证实该模型在在线学习场景下进行情感计算具有可行性和有效性。

Due to its intelligent and personalization, online learning has increasingly become a favored mainstream learning method. However, the existence of the 'affective gap' severely hampers the development of online teaching activities. It is imperative to research how to instantaneously and accurately perceive emotional cues in learning to provide guidance for improving learning performance. This paper constructs a multimodal data fusion model for emotional computation in online learning. Facial expressions, voice, and text data of subjects are collected, and emotional recognition models are employed to obtain emotional recognition results for each modality. Based on decision-level fusion, multimodal emotional computation in online learning is achieved, determining the optimal emotional computation model. The study reveals that the average recognition accuracy based on the optimal emotional computation model has increased by 14.51% compared to single-modal emotional recognition. This confirms the feasibility and effectiveness of the model in emotional computation within online learning scenarios.