With the continuous advancement of oil and gas exploration technologies, well logging lithology identification has been established as a key tool in reservoir prediction. However, traditional intelligent lithology identification methods in well logging are found to be heavily dependent on human experience and professional knowledge, which are not only considered costly and inefficient, but are also observed to underperform when handling complex nonlinear relationships in large-scale data. Therefore, the investigation of deep learning-based well logging lithology identification methods has been recognized as a research topic of significant importance. To achieve intelligent lithology recognition of well logging data, a ResNet architecture integrated with CNN (convolutional neural network) was implemented. Feature importance was evaluated using the Random Forest algorithm, and strongly correlated features were selected to construct the ResNet-CNN model. The performance of the model was systematically evaluated using confusion matrices. Experimental results demonstrate that an accuracy rate of 93.5% was achieved by the ResNet-CNN model, which is found to significantly outperform two other comparative methods. It is therefore concluded that the ResNet-CNN-based methodology demonstrates substantial application value in intelligent lithology identification of well logging data.