Abstract: In addressing the issue of debris flow disaster assessment, this paper proposes a novel lightweight convolutional neural network model, the Dual-Channel Fusion Attention Mechanism Network (DCFAMNet), designed to rapidly identifying the susceptibility of gully-type debris flows. The main contributions of this paper are as follows: Firstly, based on historical debris flow records and using Digital Elevation Maps (DEMs) and remote sensing images as data sources, a dual-channel network structure is designed as the basic technical framework. Within the DEM image feature extraction channel, a channel attention mechanism is introduced to emphasize the channel weights of the image features, while in the remote sensing image feature extraction channel, 3D convolutional blocks are employed to extract the surface information of the gullies. In the feature fusion stage, depthwise separable convolutions are used to facilitate more interaction of feature information. Secondly, the susceptibility prediction of potential threats gullies in the related basins is made, and susceptibility maps of debris flow disasters are generated. Finally, DCFAMNet visualizes the extracted deep features such as gully slope, curvature, and slope orientation. Experimental results indicate that, by integrating the DCFAMNet with GIS technology, the identification rate for debris flow gullies can reach up to 80%, with an AUC value of 0.75, indicating good performance. The best parameters of the model are retained for assessing the susceptbility scores of the relevant gullies. Through visualization analysis in ArcGIS, the debris flow disaster risk is categorized into five assessment levels. It is determined that the extremely high susceptibility and high susceptibility zones for debris flows are primarily distributed in the mainstream of the Dulong River in Gongshan County and the mainstream of the Nujiang River in Fugong County, while Lanping County is relatively safe. The findings of this research can provide valuable insights and foundations for the prevention and mitigation of debris flow disasters in mountainous regions.