The automated detection of spikes in electroencephalogram is currently a prominent area of research, with significant implications for epilepsy diagnosis. There are primarily two types of existing detection methods: signal analysis and machine learning. The former is sensitive to outliers, while the robustness of the latter’s algorithms to different data has not been fully verified. Additionally, traditional spike detection methods based on single-channel EEG are susceptible to artifact interference. In response to the limitations of existing algorithms and considering the electrophysiological characteristics of spikes, we propose a spike detection algorithm based on multi-channel data weight fusion and wavelet decomposition. Firstly, a multi-channel weight fusion method is designed using amplitude and waveform trends as feature values to enhance single-channel data according to the discharge characteristics of epileptic spikes. Secondly, the algorithm introduces wavelet decomposition to effectively extract local features from the signal and enhance its ability to detect signals with mutation characteristics. Finally, clinical EEG data collected from epileptic patients verify that the algorithm can achieve accurate detection of interictal spikes at a diagnostic accuracy rate exceeding 92.3%. Compared with traditional single-channel EEG spike detection methods, this approach offers advantages such as high accuracy and simple calculation, making it an effective technology for interictal spike detection in epilepsy.