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Debris flow susceptibility evaluation plays a crucial role in the prevention and control of debris flow disasters. Therefore, this paper proposes a convolutional neural network model named multi-level feature extraction network (MFENet). First, a dual-channel CNN architecture incorporating the Embedding Channel Attention mechanism is used to extract shallow features from both digital elevation model images and multispectral images. Subsequently, channel shuffle and feature concatenation are applied to the features from the two channels to obtain fused feature sets. Following this, a deep feature extraction is performed on the fused feature sets using a residual module improved by maximum pooling. Finally, the susceptibility index of gullies to debris flows is calculated based on the similarity scores. Experimental results demonstrate that the model exhibits favorable classification performance, with an accuracy of 73.45%. Furthermore, the percentage of debris flow valleys in high and very high susceptibility zones reaches 93.97%. [ABSTRACT FROM AUTHOR]