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Seismic data interpolation using convolutional neural networks (CNNs) suffers from accuracy limitations due to the inter‐band interference across different frequency bands, which negatively affects subsequent inversion and interpretation. To address this limitation, we propose a multi‐band strategy that first decomposes the seismic data into multiple sub‐bands through frequency filtering. Independent CNN models are then used to process each specific frequency band to isolate spectral interference. We focus on regularly missing shots interpolation, assuming that dense receiver arrays are available during seismic acquisition with sparse shots. As for the training data preparation, the spatial reciprocity of Green's function is considered, which guarantees the similarity between common shot gathers (CSGs) and common receiver gathers (CRGs). The available dense CSGs are used to train networks using the multi‐band‐assisted training strategy. The resulting optimized independent models are then employed to reconstruct missing shots in sparse CRGs for each frequency band separately. Interpolated multi‐band data are finally fused by summation to obtain the full‐band result. Numerical experiments on synthetic and field data demonstrate that the proposed multi‐band‐assisted training strategy provides superior interpolation accuracy compared to traditional full‐band training, particularly in mitigating cross‐band interference.