The morbidity and mortality rates of liver cancer in China remain concerning, emphasizing the urgent need for early diagnosis and treatment to improve the situation. In response to this challenge, we propose a novel 3D ELD_MobileNetV2 hepatic nodule classification model that incorporates attention mechanism and dilated convolution. This model is specifically designed to classify abdominal dynamic enhanced magnetic resonance images. Firstly, the two-dimensional network structure was extended to three dimensions to avoid the loss of spatial information during feature extraction of magnetic resonance imaging (MRI). Secondly, based on the local cross-channel interaction strategy, an efficient channel attention mechanism combining local features and global features was embedded in the bottleneck structure of MobileNetV2 network to enhance the key feature extraction capability. Then, 3D dilated structure was introduced into depthwise convolution to improve the receptive field of the convolution kernel. Meanwhile, the original activation function was replaced with Leaky ReLU6 activation function to improve the model's robustness. The model was tested and validated on a dataset comprising 120 patients (30 cases each of hepatitis, cirrhotic nodules, dysplastic nodules, and hepatocellular carcinoma). Experimental results demonstrate significant improvements over the original MobileNetV2, with an accuracy increase of 0.083. Compared to other networks, including AlexNet, VggNet16, ResNet50, ConvNeXt, the 3D ELD_MobileNetV2 achieves superior performance, with the accuracy of 0.792, F1_Score of 0.688, 0.750, 0.848, 0.872, micro-average AUC of 0.954, and macro-average AUC of 0.948. The findings highlight the effectiveness of the proposed model in classifying liver nodules across different stages. This advancement is expected to facilitate early diagnosis of liver cancer and improve clinical outcomes.