Abstract: Real-time diagnosis of the effectiveness of the bridge plug ball seat setting is a key step in the staged fracturing of horizontal wells. If the ball seat setting fails, follow-up operations cannot proceed normally. Currently, manual observation of wellhead pressure changes is primarily relied upon, making it difficult to quickly and accurately identify key characteristics. To address this, a combination of expert qualitative judgment and quantitative feature mining of setting data was implemented. Sliding window data was segmented to form 5792 sets of labeled data. A long short-term memory (LSTM) neural network, using a two-dimensional input of wellhead pressure and displacement, was selected. An intelligent diagnosis model for evaluating the effectiveness of the fracturing ball seat setting was established, utilizing an under-sampling balanced dataset to improve the model’s prediction accuracy. The results show that the setting data exhibits a clear three-stage characteristic: a steep rise, a steep drop, and a gentle rise in wellhead pressure. If the wellhead pressure lacks any of these stage characteristics, it indicates an invalid setting. The wellhead pressure slope exhibits a wide distribution range, making it difficult to form explicit rules for accurate diagnosis. Artificial intelligence technology is used to learn the valid/invalid setting data characteristics from various wellhead pressure forms, producing diagnosis results per second with an accuracy of 96.8% for the test set and 84.3% for the validation set. The findings are expected to provide a method for real-time and automatic diagnosis of the effectiveness of the bridge plug ball seat setting.