中文情感分析旨在挖掘出中文文本中的主观情感。目前大多数基于深度学习的中文情感分析模型需要依赖大规模的标注数据去训练，同时深度学习模型在实际应用当中很容易受到对抗性扰动的影响，导致模型的性能下降。针对上述问题，本文提出了基于模型不可知元学习与对抗训练的中文情感分析模型，能够在小规模的数据集下利用元学习加速模型收敛，同时生成对抗样本对模型进行对抗训练，提升模型的抗干扰能力，实验证明模型取得了出色的表现。

Chinese affective analysis aims to dig out the subjective emotion in Chinese text. At present, most Chinese affective analysis models based on deep learning need to rely on large-scale labeled data for training. Meanwhile, deep learning models are easy to be affected by adversarial disturbance in practical applications, resulting in the degradation of model performance. In response to the above issues, this paper proposes a Chinese affective analysis model based on model-agnostic meta-learning and antagonistic training, which can accelerate the convergence of the model using meta learning under small-scale datasets, and generate confrontation samples to conduct confrontat-ion training on the model, improving the anti-interference ability of the model. Experiments have shown that the model has achieved excellent performance.