Abstract: In recent years, a number of vertical shaft tilt deformation and breakage disasters have occurred in the eastern mining areas of China, which have seriously affected mine safety and production. In response to the tilting and damage disasters of deep vertical shafts in thick water-bearing loose layers, the tilting and deformation monitoring of shafts was carried out by taking the deep vertical shaft (800 m) of a mine in Lunan as the research object, studying the spatial and temporal change characteristics of shaft tilting, and analyzing the main influencing factors of shaft tilting; based on this, based on the deep learning theory, four types of deep learning method, namely, recurrent neural network (RNN), long and short-term memory network (LSTM), gated recurrent unit (GRU), and one-dimensional convolutional neural network (1DCNN), were used. unit (GRU), and one-dimensional convolutional neural network (1DCNN) to construct a wellbore tilt deformation prediction model, and compare the prediction results with the measured values to analyze the accuracy of the wellbore deformation prediction model, validate the reliability of the model, studied overall wellbore and critical area prediction effects, and carry out engineering applications. The study shows that: ① The wellbore tilt mainly occurs in the loose layer, the tilt value decreases linearly from shallow to deep, and is biased towards the side of the extraction zone, with a maximum of 352 mm, and the deformation of the bedrock layer is smaller, with a maximum of 88 mm; the increase in the range of deformation propagation in the thick loose layer caused by the mining, and the change of seepage hydrophobicity of the aquifer at the bottom along the wall of the well and the seepage field of the groundwater are the main causes of the tilted deformation of the wellbore. ② The Spearman correlation coefficient between the model and the measured value is 0.978 at the maximum and 0.867 at the minimum;the maximum difference between the four models and the field measured offsets is 0.043 m, the mean absolute error E_MA is within 0.003–0.009 m, and the root mean square error E_RMS is within 0.004–0.011 m. The overall prediction is optimized by the 1DCNN model, and the main tilting direction (The prediction accuracy of the main inclined direction (east-west direction, which is inclined to the side of the mining area) is slightly lower than that of the direction with smaller deformation amount (north-south direction), and all of them can meet the engineering needs. ③ The overall prediction curve of the wellbore is consistent with the actual tilt direction, and the average values of E_MA and E_RMS of the wellhead and loose bedrock interface are 0.005 m and 0.006 m. The accuracy of the wellbore bottoming is a little bit lower, with the corresponding values of 0.012 m and 0.013 m. The wellbore characteristic area and overall prediction effect are good, indicating that the wellbore deformation prediction model based on deep learning has good prediction ability. The research results have been effectively applied in the wellbore grouting repair and management project, which provides technical reference and data support for the safe management of wellbore, and provides engineering practical experience for similar projects.