This paper employs deep learning algorithms based on actual wellbore trajectory parameter data and well logging data collected from the Weiyuan block in Sichuan. An algorithmic framework capable of real-time predicting encountered strata during drilling is established. Firstly， this framework utilizes a time-series prediction algorithm to forecast the subsequent wellbore trajectory. Subsequently， based on this prediction， it achieves the forecast of strata gamma values. Finally， the identification of encountered strata types is accomplished by utilizing the predicted strata gamma values. To enhance the predictive accuracy of the entire algorithmic framework， various algorithms are employed and compared at each stage. The results demonstrate that real-time prediction using recurrent neural networks， gamma value regression prediction through random forests， and strata classification prediction using support vector machines yield the best training outcomes. The predictive accuracies reach 0.81， 0.83 and 81.8%， respectively. The real-time anticipation of encountered strata during the geologically guided process provides on-site data support and auxiliary judgment means. This contributes to the more reliable and effective achievement of geological objectives in practical engineering.