针对无人机通信领域中文命名实体识别样本不足、语义易混淆和实体边界模糊等问题，提出了一种多特征融合的中文命名实体识别模型RCPAC（roberta-wwm-ext-large-CNN-PositionAwareAttention-BiLSTM-CRF）。首先，使用无人机通信领域词典动态融入到 roberta-wwm-ext-large模型获得融合无人机领域词信息的全局特征，并在卷积神经网络（Convolutional Neural Network，CNN）层引入挤压激励网络（SENet，SqueezeandExcitation Networks）注意力机制，提取关键特征，在双向位置感知注意力机制中设计区域划分的前向、后向两个注意力模块，以捕捉实体边界特征；然后将融合词信息的全局特征、CNN层提取的多尺度局部特征和双向位置感知注意力机制获取的位置信息与实体特征进行动态权重融合；接着通过双向长短期记忆网络（Bidirectional Long Short-Term Memory，BiLSTM）层建立长序列依赖并过滤掉冗余特征；最后通过条件随机场（Conditional Random Fields，CRF）解码层输出最佳标注序列。通过对比实验和消融实验证明了该模型的有效性和优越性。在自主构建的小规模无人机通信领域数据集上，与其他模型相比，RCPAC模型的F1值提升了1.01%~9.95%。此外，在CCKS2021中文地址要素解析和MSRA数据集上，模型的F1值分别达到92.15%和95.56%，证明了该模型的泛化性和有效性。

Aiming at the problems of insufficient samples， semantic ambiguity and blurred entity boundaries in Chinese named entity recognition for UAV communication， this paper proposes a multifeature fusion Chinese named entity recognition model RCPAC （roberta-wwm-ext-large-CNN-PositionAwareAttention-BiLSTM-CRF）. Firstly， the UAV communication domain dictionary is dynamically integrated into the robertawwmextlarge model to obtain global features fused with domainspecific lexical information. A SqueezeandExcitation Networks （SENet） attention mechanism is introduced into the Convolutional Neural Network （CNN） layer to extraction key feature. Within the bidirectional positionaware attention mechanism， two regiondivided forward and backward attention modules are designed to capture entity boundary features. Then， dynamic weightbased fusion is conducted on the global features with lexical information， multiscale local features extracted by the CNN layer， as well as position information and entity features obtained from the bidirectional positionaware attention mechanism. Next， the Bidirectional Long ShortTerm Memory （BiLSTM） layer is used to model longrange sequence dependencies and filter redundant features. Finally， the Conditional Random Fields （CRF） decoding layer outputs the optimal label sequence. Comparative experiments and ablation experiments verify the effectiveness and superiority of the proposed model. On the selfconstructed smallscale UAV communication dataset， the F1score of the RCPAC model is improved by 1.01%-9.95% compared with other baseline models. In addition， the model achieves F1scores of 92.15% and 95.56% on the CCKS2021 Chinese address element parsing dataset and the MSRA dataset respectively， demonstrating its generalization ability and effectiveness.