Abstract: The lightning process generates multiple types of lightning electric field waveforms. Traditional classification methods based on waveform parameters are prone to make misclassification. To address this issue, we proposed a method of VLF/LF lightning electric field signal classification based on a multi-channel convolutional neural network. This method uses a deep network to directly process the field waveforms, reducing dependency on prior knowledge. The network was constructed with multiple convolutional kernels to effectively extract the multi-scale waveform features. The shortcut connections were introduced to accelerate model convergence. Based on the data collected in Hefei, a training dataset of four typical waveforms, namely, return stroke, preliminary breakdown, narrow bipolar event, and intracloud, was established. The training results show that the model achieves an accuracy of 99.4%. Compared with classic machine learning methods and deep learning models, the proposed model performs better in classification accuracy and training convergence speed. By using the knowledge distillation method, a model suitable for low-computing-power platforms can be obtained. The distilled model takes only 59 ms for single classification, with a 66% reduction in computing power requirements and a classification accuracy of 99.0%, demonstrating reliable application of the proposed model on low-computing-power platforms.