Stratigraphic recognition is the basis for the research of oil and gas reservoir exploration. Traditional stratigraphic identificationis done manually by geologists?based on their own knowledge and experience,and this geologists-led manual interpretation is subjective,time - consuming,and can introduce artificial bias. Deep learning has advantages in solving complex nonlinear problems,and there iscurrently no effective deep learning method to solve formation recognition. For logging - stratigraphic recognition, a stratigraphic intelligent recognition method based on feature engineering and one-dimensional convolutional neural network is proposed. Firstly, the original curve is reconstructed by INPEFA and median filtering,the stratigraphic trend and edge features of the original curve are betterextracted,and the K-means clustering algorithm is used to extract the spatiotemporal correlation clustering features of the reconstructedmatrix and the original curve. Then,taking the original curve features,INPEFA curves,median filtering features and clustering features asinputs,the current deep stratigraphic prediction type is obtained based on the one-dimensional convolutional neural network. Comparedwith the long short - term memory network ( LSTM) and traditional machine learning methods, the formation intelligent recognitionmethod has better performance and robustness in the recognition of strata. The proposed method can effectively identify strata, therecognition accuracy reaches 92. 82% ,and the strata division is completed at the same time as identifying the strata.