In the field of oil and gas development, the sealing performance test of oil casing after installation is particularly important.Torque sequence data is an important basis for judging the sealing performance of the oil casing, which can be used to judge whether the buckle is qualified. In order to identify and classify the sealing performance of the oil casing by using the information of the buckled torque sequence data, a new network model was built which named PSE-TCN network based on the TCN model integrated with position encoding and self-attention mechanisms. By comparing the accuracy of results under different strategies, the learning process of the model was demonstrated. The effectiveness of this method was validated by comparing it with other network models. Experimental results show that torque sequence recognition accuracy was significantly improved by the PSE-TCN network compared with other classical network models and several improved TCN models. The recognition accuracy of this model achieved 93.41% on the self-made UCR_whorl dataset.