Accurate identification and classification of lithofacies provide essential support for reservoir evaluation, fluid identification, and reservoir characterization, serving as a critical factor in locating high-quality reservoir development zones and favorable hydrocarbon accumulation areas. Core observation and thin-section analysis are the primary sources of first-hand data for direct lithofacies identification. However, due to limitations in core availability and high analytical costs, lithofacies recognition often requires the integration of well logging data. Compared to traditional well logging-based lithofacies identification methods, deep learning offers the advantages of automated and efficient lithofacies recognition, with improved interpretative accuracy and reduced uncertainty. To address these challenges, this study reviews the application of deep learning in lithofacies recognition using well logging data, systematically summarizing the research findings from two aspects: application conditions and effectiveness. Specifically, the study focuses on lithofacies recognition models based on conventional well logging and models integrating conventional and electrical imaging logging. Drawing on previous research, this paper proposes a preliminary intelligent lithofacies recognition model tailored to the complexities of carbonate lithofacies. Finally, it highlights the challenges in applying intelligent recognition models to well logging lithofacies identification and discusses future development trends in this field.