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Abstract: With the successful application and breakthrough of deep learning technology in image segmentation, there has been continuous development in the field of seismic facies interpretation using convolutional neural networks. These intelligent and automated methods significantly reduce manual labor, particularly in the laborious task of manually labeling seismic facies. However, the extensive demand for training data imposes limitations on their wider application. To overcome this challenge, we adopt the UNet architecture as the foundational network structure for seismic facies classification, which has demonstrated effective segmentation results even with small-sample training data. Additionally, we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range. The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries. Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results, as evidenced by various evaluation metrics for image segmentation. Obviously, the classification accuracy reaches an impressive 96%. Furthermore, the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method, which accurately defines the range of different seismic facies. This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.