The diagnosis and treatment of brain diseases represent the forefront of brain science research, with EEG-related research occupying a uniquely significant position. In recent years, deep learning technology has been widely applied to the study of EEG signals, yet the integration of information from multiple EEG channels remains a challenging task. Based on the dynamic routing algorithm, this study established a deep neural network. Subsequently, leveraging this network, an epileptic seizure recognition method, VarChanNet, was proposed. Seizure recognition experiments were conducted using the Bonn and CHB-MIT databases. The experimental results demonstrate that the proposed VarChanNet method maintains high recognition accuracy even when the number of channels involved in the recognition process changes. It reliably functions on both the Bonn and CHB-MIT databases, indicating its potential for generalization. Furthermore, the method provides recommendations for channel selection during the recognition process. For instance, in the case of CHB-MIT, Channel 21 can be selected for single-channel recognition, Channels 2 and 3 for dual-channel, and Channels 1, 2, and 3 for triple-channel epileptic seizure recognition. In a word, the proposed VarChanNet method enables the fusion of information from different EEG channels, supporting recognition tasks even when the number of channels varies. It offers a new perspective for EEG analysis and holds the potential for generalization.