Research on integrating statistical knowledge into deep learning models for earthquake forecasting has been limited. Traditional deep learning models require extensive parameter learning from scratch. This study proposes a Spatio-Temporal Convolutional (STC) model that incorporates spatio-temporal decay prior knowledge derived from the Epidemic-Type Aftershock Sequence (ETAS) into a convolutional kernel. This allows the STC model to have the prototype to learn the pattern of mainshocks to trigger aftershocks at the beginning of training, with only 4 neurons to fine-tune it. In California, the STC and the ETAS model are conducted for forecasting next-day earthquakes with magnitudes of M ≥3, M ≥4, and M ≥5. Both performances were assessed using the Receiver Operating Characteristic (ROC) curve, the Precision-Recall (PR) curve, and the Parimutuel Gambling Score (PGS). The evaluation results indicate that the STC model surpasses ETAS in forecasting next-day earthquakes not accidental. Furthermore, our analysis suggests that including earthquakes below the complete magnitudes can enhance the STC model's classification performance, as small earthquakes also contain information about future earthquakes.