To make full use of the long time series information of multiple monitoring factors obtained by a drilling monitoring platform and implement accurate prediction of sticking accidents in offshore oil drilling, this study proposes a deep convolution sticking prediction method based on joint modeling of multi-factor long time series information (CNN-MFT). It uses the self-attention mechanism and a CNN to jointly model the time series information of multiple monitoring factors. Meanwhile, it considers the specific value of each factor at the current moment and the historical time series information of each factor to achieve accurate sticking prediction. Verification and comparison are conducted with actual monitoring data on the offshore drilling platform. Compared with the eight commonly used sticking prediction methods such as those based on random forest (RF) and support vector machine (SVM), the proposed CNN-MFT achieves the best accuracy of sticking accident prediction under different training sample proportions, 50% and 70% for example. Meanwhile, it is also more stable. This method provides key algorithm support for applications of offshore oil accident prediction.