Pore pressure is a key parameter in oil and gas exploration and development. Accurate prediction is crucial for ensuring drilling safety and improving operational efficiency. Traditional models and shallow machine learning methods often lack effective integration of lithological spatial information, which limits their generalization performance. This paper proposes a deep graph attention network method (CLG) that incorporates lithology information for pore pressure prediction. The model takes multiple inputs including well depth, equivalent circulating density (ECD), mechanical drilling parameters (rate of penetration and weight on bit), natural gamma ray, porosity, and lithology encoding. Most data are obtained from the logging while drilling (LWD) system, and are processed by boxplot cleaning and normalization to improve quality. CNN extracts local features, LSTM captures temporal dependencies, and the graph attention mechanism integrates lithological structural information. Application in a Bohai exploration area shows that the CLG model achieves excellent performance across multiple wells (R2=0.997 9, MSE=0.003 5, RMSE=0.059 6, MAE=0.048 1), improving accuracy by about 15% and 7.4% compared to the Eaton method and various shallow models, respectively, with enhanced generalization ability. This method effectively advances pore pressure modeling with lithology awareness, providing reliable support for oil and gas drilling under complex geological conditions.