闪电类型识别在雷电探测和气象防灾减灾等领域具有重要的作用。目前闪电类型识别面临数据多样性、波形特征复杂等难题，限制了识别算法的准确性和泛化能力。提出了一种基于多尺度残差网络和注意力机制的闪电波形多分类算法MSRES-SA（multi-scale residuals and self-attention），旨在提高闪电波形识别的准确性。首先构建了一个多尺度残差特征提取模块，用于提取闪电波形在时间维度上不同尺度的信息，并使用残差连接来增强模型的表征能力。然后使用注意力机制来动态加权重要特征，捕捉波形序列中的长距离关联。实验结果表明，MSRES-SA算法的平均识别精度为99.35%，在多个闪电波形类别识别中优于基线模型，并通过消融实验证明了多尺度残差模块和注意力模块在闪电波形识别任务中的有效性。

Lightning type recognition plays a crucial role in the fields of lightning detection and meteorological disaster prevention and mitigation. Currently, lightning type recognition faces challenges such as data diversity and complex waveform features, which limit the accuracy and generalization ability of recognition algorithms. A lightning waveform multi-classification algorithm, multi-scale residuals and self-attention (MSRES-SA), based on a multi-scale residual network and attention mechanism, was proposed to enhance the accuracy of lightning waveform recognition. Initially, a multi-scale residual feature extraction module was constructed to extract information from lightning waveforms at different scales in the time dimension, and residual concatenation was employed to enhance the model's representational capability. Subsequently, the attention mechanism was utilized to dynamically weight important features, capturing long-range dependencies within the waveform sequence. Experimental results demonstrate that the MSRES-SA algorithm achieves an average recognition accuracy of 99.35%, outperforming the baseline model in the recognition of multiple lightning waveform categories. The effectiveness of the multi-scale residual module and the attention module in the lightning waveform recognition task is further validated through ablation experiments.